Tryphena Ow -Thesis

1
Tryphena Ow, An Examination of the Market Timing Ability of Mutual Funds using
Quarterly Trades
ACKNOWLEDGEMENTS
The journey to complete my thesis was both trying and fulfilling, I would not have
made it this far without the love and support from my family, lecturers and friends.
First and foremost, I would like to express my immense gratitude to my supervisor,
Professor Dominic Gasbarro. His tireless guidance, constructive suggestions and advice had
inspired me to strive for the best. Without his inexhaustible patience and guidance, this thesis
would not have been possible to accomplish. Under his abounding guidance, I have also
acquired new skills and insights, not only in academic studies but vigour in life.
Next, this valuable opportunity I have today, I owe to Professor Andrew Taggart. I am
very grateful to be awarded the Vice Chancellor’s SG50 Honours Scholarship. This award
has granted me a valuable opportunity to further my education abroad.
In addition, I would like to sincerely thank Mr Stephen Klomp for his hospitality and
kind guidance during my stay in Perth. I would also like to thank my lecturers, Dr Amy
Huang, Miss Thanesvary Subraamanniam and Miss Michelle Gander for their patience and
guidance throughout my units.
Last but not least, to my cherished family, I am deeply thankful and appreciative of
their boundless love, unwavering support and encouragement throughout this journey.

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ABSTRACT
Past research primarily focus on evaluating market timing abilities using the returns
and stockholdings of mutual funds. We examine the market timing abilities of fund managers
using the trade proportions of mutual funds. These are statistically significant trade
proportions that encompass beta, sentiment beta and momentum. Trade proportions provide
insights on the direction that the fund manager was pursuing. Market and systemic risk
indicators are important for our study as they reflect the overall performance of the market
and the economy. We compare between the values of these indicators and the values of our
statistically significant trade proportions to evaluate if these values are highly correlated
during various market cycles. Using correlation and regression analysis, we examine the
relation between the trade proportions (dependent variable), the market and systemic risk
indicators (independent variables). We have also taken into consideration of certain
conditions that might affect the adjustments of these trade proportions and conducted some
preliminary and robust tests. In general, we expect that prior to a bull (bear) market, fund
managers will adjust their portfolios towards positive (negative) trade proportions.
Furthermore, majority of past studies had evaluated market timing abilities only during
recession periods therefore our study period between 1991 and 2012 has incorporated both
recession and boom periods to avoid biasness in results. However, similar to previous
findings, these trade proportions did not demonstrate superior market timing abilities.
Although no significant market timing abilities were exhibited, momentum trade proportions
displayed the most significant correlation and regression results. We observed an inverse
relationship between the positive momentum trade proportions and the momentum index.
This is consistent with fund managers having pursued a contrarian strategy.

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CONTENTS
ACKNOWLEGDEMENTS 1
ABSTRACT 2
CONTENTS 3-7
FIGURES 8
GRAPHS 9
TABLES 10
1 INTRODUCTION 13-18
1.1 Introduction 13-18
2 LITERATURE REVIEW 19-60
2.1 Introduction 19
2.2 Overview of Literature 19-21
2.3 Characteristics of Mutual Funds 21-22
2.4 Mutual Fund Performance- Market Timing 22-23
2.4.1 Timing using Convex Relationship between Fund Returns and
Market Returns
23-25
2.4.2 Stationary Beta versus Non-Stationary Beta in Bull and Bear Markets 25-34
2.4.3 Evaluating Market Timing Abilities simultaneously with Security
Section Abilities
34-37
2.4.4 Free from Beta Estimates 37-39
2.4.5 Portfolio Performance Measures without Benchmarks 39-41
2.4.6 Volatility Timing 41-42
2.4.7 Downside of Returns Chasing Behaviour 43-44
2.4.8 Persistence in Fund Performance 44-46
2.4.9 Business Cycles and Predictability Skills 46-47
2.4.10 Stockholdings versus Trades 47-53
2.4.10.1 Market Timing Abilities 48-51
2.4.10.2 Stock Selection Abilities 51-54
2.4.11 Downside of Risk Shifting Behaviour 54-55

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2.4.12 Successful Market Timing Abilities 56-57
2.5 Overview of Contrarian Strategies 57-58
2.5.1 Identifying Contrarian Strategies in Mutual Fund Trades 59
2.6 Conclusion of Literature Review and Motivation of Present Study 59-60
3 METHODOLOGY 61-79
3.1 Introduction 61
3.2 Overview of Methodology 61-63
3.3 Data Description 63
3.3.1 Bull and Bear Markets 63-64
3.3.2 Recession and Boom Periods 64-66
3.3.3 Four States of Bull and Bear Markets 66-68
3.4 Trades 68
3.4.1 Identifying Market Timing Trades 68
3.4.1.1 Formula for Identifying Market Timing Trades 68-69
3.4.2 Identifying Sentiment Beta Timing Trades 69
3.4.3 Identifying Momentum (Contrarian) Trades 69-70
3.5 Importance of Indices 70-71
3.5.1 Description of Indices 72-74
3.5.1.1 The S&P 500 Index 72
3.5.1.2 The Baker & Wurgler’s Sentiment Index 72
3.5.1.3 The S&P 500 Momentum Index 73
3.5.1.4 The S&P 500 Quality Index 73
3.5.1.5 The S&P 500 Growth Index 73-74
3.5.1.6 The S&P 500 Low Volatility Index 74
3.5.1.7 The S&P 500 High Beta Index 74
3.5.2 Systemic Risk Measures 74-77
3.5.2.1 Brief Description of Systemic Risk Measures (19 Elements) 75-77
3.6 Sources of Data and Availability 78

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3.7 Trades’ Correlation with Indices 78-79
3.8 Conclusion of Methodology 79
4 RESULTS AND DISCUSSION 80-133
4.1 Introduction 80
4.2 Overview of Results and Discussion 80-83
4.2.1 Market Indicators 81
4.2.2 Systemic Risk Indicators 81-82
4.2.3 Overview of Analysis (Schematic Diagram) 82-83
4.3 Fund Quarters, Significant Fund Quarters and Proportions 83-85
4.3.1 Descriptive Statistics of Significant Fund Quarters and Proportions 86-87
4.4 Descriptive Statistics of Market and Systemic Risk Indicators 87-91
4.4.1 Descriptive Statistics (In Months) 87-90
4.4.2 Descriptive Statistics (In Quarters) 90-91
4.5 Performance of Market Indicators 92-99
4.6 Correlation Testing 99
4.6.1 Correlation Testing between Market Indicators (Main Market
Indicators and Sub-Market Indicators)
100-102
4.6.2 Correlation Testing between Systemic Risk Indicators 102
4.6.2.1 Brief Description of the Selected Systemic Risk Indicators 102-107
4.7 Final Selection of Market and Systemic Risk Indicators 107-109
4.8 Correlation and Regression Analysis between Trade Proportions
(DV) and Indicators (IV)
109-111
4.9 Overall Test for Correlation and Regression Analysis 111-117
4.10 Preliminary Test 117-129
4.10.1 Market Beta Trade Proportions 118-121
4.10.1.1 Market Index 118-119
4.10.1.2 Market “Return” Indicator 119-121
4.10.2 Sentiment Beta Trade Proportions 121-124
4.10.2.1 Sentiment Index 121-122

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4.10.2.2 Sentiment “Return” Indicator 122-124
4.10.3 Momentum Trade Proportions 125-128
4.10.3.1 Momentum Index 125-127
4.10.3.2 Momentum “Return” Indicator 127-129
4.11 Summary Table of Significant Results based on Overall Analysis
and Preliminary Tests
130
4.12 Conclusion of Results and Discussion 131-133
5 ROBUST TESTING 134-164
5.1 Introduction 134
5.2 Overview of Robust Testing 134-137
5.3 Beta Trade Proportions and the Market “Return” Indicator 137-140
5.3.1 Test (1): Magnitude of Change 137-138
5.3.2 Test (2): Changes in Standard Deviation 138-139
5.3.3 Test (3): Changes in Signs 139
5.3.4 Test (4): Persistence in Index 139-140
5.4 Beta Trade Proportions and the Market Index 142-143
5.4.2 Test (4): Persistence in Index 142
5.5 Sentiment Beta and the Sentiment “Return” Indicator 143-146
5.5.1 Test (3): Changes in Signs 143
5.6 Sentiment Beta and the Sentiment Index 146-147
5.6.1 Test (3): Changes in Signs 145-146
5.7 Momentum Trade Proportions and the Momentum “Return”
Indicator
148-149
5.8 Momentum Trade Proportions and the Momentum Index 149-151
5.9 Multiple Regression Analysis 151-160

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5.9.1 Market Beta 151-54
5.9.1.1 Positive Market Beta Proportions with the Market “Return”
Indicator
151-153
5.9.1.2 Positive Market Beta Proportions with the Market Index 153-154
5.8.2 Sentiment Beta 154-157
5.9.2.1 Positive Sentiment Beta Proportions with the Sentiment “Return”
Indicator
154-156
5.9.2.2 Positive Sentiment Beta Proportions with the Sentiment Index
Indicator
156-157
5.9.3 Momentum Trades 157-160
5.9.3.1 Positive Momentum Proportions with the Momentum “Return”
Indicator
157-159
5.9.3.2 Positive Momentum Proportions with the Momentum Index 159-160
5.10 Summary Table of Significant Results based on Robust and
Multiple Regression Tests
161
5.11 Conclusion of Robust Testing 162-164
6 CONCLUSION 165-79
6.1 Introduction 165
6.2 Overview of Conclusion 165-167
6.3 Significant Research Findings 167-168
6.4 Limitations of the Research 168
6.6 Areas of Future Research 169-170
6.6 Summary of Study 170-171
REFERENCES 169-179

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FIGURES
Figure 1.1: Schematic Diagram: Overview of Methodology 18
Figure 2.1: The Characteristic Line of a Fund that Outguess the Market (Treynor
and Mazuy, 1966)
23
Figure 3.1: Schematic Diagram: Overview of Methodology 62
Figure 4.1: Trades, Market Indicators and Systemic Risk Indicators 83
Figure 4.2: Final Selection of Indicators for Analysis 108
Figure 4.3: Statistically Significant Trades and their Respective Indicators 109
Figure 5.1: Types of Robust Test Conducted 136

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GRAPHS
Graph 4.1: Price Fluctuations of the Market Index, June 1991 to September 2012 92
Graph 4.2: Price Fluctuations of Market “Return” Indicator, July 1991 to
September 2012
93
Graph 4.3: Changes in the Sentiment Index Values, June 1991 to March 2011 95
Graph 4.4: Changes in the Values of the Sentiment “Return” Indicator, July 1991
to March 2011
96
Graph 4.5: Changes in the Momentum Index Values, September 2006 to
September 2012
97
Graph 4.6: Changes in the Values of the Momentum “Return” Indicator, October
2006 to September 2012
98

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TABLES
Table 3.1: Bull and Bear Market Durations throughout the Trading Period
between July 1991 and October 2012
63
Table 3.2: Recession and Boom Durations throughout the Trading Period
between July 1991 and October 2012
65
Table 3.3: Data Sources, Availability and Types of Data 78
Table 4.1: Trades- Number of Fund Quarters, Significant Fund Quarters and
Proportions
85
Table 4.2: Descriptive Statistics of Statistically Significant Fund Quarters and
Proportions
86-87
Table 4.3: Descriptive Statistics of Market and Systemic Risk Indicators
(Presented in Months)
89-90
Table 4.4: Descriptive Statistics of Market and Systemic Risk Indicators
(Presented in Quarters)
91
Table 4.5: Correlation of Market Indicators: 73 Monthly and 25 Quarterly
Observations, September 2006 – September 2012
101-102
Table 4.6: Correlation between Systemic Risk Indicators: 247 Monthly
Observations and 83 Quarterly Observations, June 1991 to
December 2011
104-105
Table 4.7: Significant (at 0.01 Level) Results of Positive and Negative
Correlations between the Selected Systemic Risk Indicators: 83
Quarterly Observations, June 1991 to December 2011
106-107
Table 4.8: Overall Correlation and Regression between Trade Proportions and
Indicators
115-117
Table 4.9: Individual Correlation and Regression Analysis between Market
Beta Trades (Proportions) and the Market Index – June 1991 to
September 2012
118-119
Table 4.10: Individual Correlation and Regression Analysis between Market
Beta Trades (Proportions) and the Market “Return” Indicator – July
1991 to September 2012
120-121
Table 4.11: Individual Correlation and Regression Analysis between Sentiment
Beta Trades (Proportions) and the Sentiment Index– June 1991 to
March 2011
122

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Table 4.12: Individual Correlation and Regression Analysis between Sentiment
Beta Trades (Proportions) and the Sentiment “Return” Indicator–
July 1991 to March 2011
124
Table 4.13: Individual Correlation and Regression Analysis between
Momentum Trades (Proportions) and the Momentum Index –
September 2006 to September 2012
127
Table 4.14: Individual Correlation and Regression Analysis between
Momentum Trades (Proportions) and the Momentum “Return”
Indicator – October 2006 to September 2012
129
Table 4.15: Significant Results based on Overall Analysis and Preliminary Tests 130
Table 5.1: Number of Quarters in relation to Market “Returns”- July 1991 to
September 2012
138
Table 5.2: Empirical Rule for Normally Distributed Data 139
Table 5.3: Robust Testing between Proportions of Beta Trades based and the
Market “Return” Indicator
141
Table 5.4: Robust Testing between Proportions of Beta Trades and the Market
Index
143
Table 5.5: Robust Testing between Proportions of Sentiment Beta Trades and
the Sentiment “Return” Indicator
145-146
Table 5.6: Robust Testing between Proportions of Sentiment Beta Trades and
Sentiment Index
147
Table 5.7: Robust Testing between Proportions of Momentum Trades and the
Momentum “Return” Indicator
149
Table 5.8: Robust Testing between Proportions of Momentum Trades and the
Momentum Index
150
Table 5.9: Robust Testing for Proportions of Beta Trades with the Market
“Return” Indicator and 11 Systemic Risk Indicator
152-153
Table 5.10: Robust Testing for Proportions of Beta Trades with the Market
Index and 11 Systemic Risk Indicator
154
Table 5.11: Robust Testing for Proportions of Sentiment Beta Trades with the
Sentiment “Return” Indicator and 11 Systemic Risk Indicator
155-156

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Table 5.12: Robust Testing for Proportions of Sentiment Beta Trades with the
Sentiment Index and 11 Systemic Risk Indicator
157
Table 5.13: Robust Testing for Proportions of Momentum Trades with the
Momentum “Return” Indicator and 11 Systemic Risk Indicator
158-159
Table 5.14: Robust Testing for Proportions of Momentum Trades with the
Sentiment Index and 11 Systemic Risk Indicator
160
Table 5.15: Significant Results based on Robust and Multiple Regression Tests 161

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CHAPTER 1
INTRODUCTION
1.1 Introduction
The performance measures for market timing abilities of fund managers has been a
predominant topic. Market timing is the ability of fund managers to tilt their portfolios in
accordance to the anticipated market trends to exploit returns. Common market trends are the
bullish and bearish markets. During bullish markets, fund managers can take advantage of the
market by buying high beta stocks and selling low beta stocks. In contrast, during bearish
markets, fund managers can take advantage of the market by buying low beta stocks and
selling high beta stocks.
In relation to predictability skills, fund managers can monitor the performance of the
market with the assistance of market indicators as they reflect the market movements. If the
index level of the S&P 500 market index consistently increases (decreases), we can anticipate
a bullish (bearish) market. However, market timing can also be a form of risk as the cost of
adjusting a portfolio may not be justified for the gains in return. Furthermore, portfolio tiling
may not necessarily suggest that fund managers are taking advantage of fluctuating
investment opportunities but a signal of ill motivated trades from mediocre abilities of fund
managers or agency issues (Huang, Sialm and Zhang, 2011). There is also a possibility of
mistiming which exposes funds to underperformance by selling (buying) stocks with high
(low) betas before a bullish (bearish) market period.
Early studies identified market timing abilities by evaluating the returns of mutual
funds. Treynor and Mazuy (1966) studied the returns of mutual funds on their historical
success of forecasting variations in the stock market. They reported that the fund returns and
the market returns had a convex relationship. Successful market timers would increase their

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exposure to the market during bullish markets and decrease their exposure during bearish
markets. However, they did not consider how the systematic risk which is measured by beta
could vary in bullish and bearish markets. Subsequently, researchers incorporated the use of a
non-stationarity beta to evaluate market timing abilities of fund managers (Fabozzi and
Francis, 1979; Kim and Zumwalt, 1979; Miller and Gressis, 1980; Chen, 1982). A non-
stationary beta gives allowance for the increase in risk exposure. However, there were still no
significant evidence of market timing abilities.
Attention has been shifted to the evaluation of the performance of stockholdings and
trades to examine the predictive abilities of fund managers. Jiang, Yao and Yu (2007) found
positive market timing abilities when quarterly portfolio holdings were applied to a single
index model. However, Elton Gruber and Blake (2012) re-examined their study and argued
that using quarterly portfolio holdings may have resulted in an inaccurate conclusion of
market timing abilities as a vast number of trades were not captured in their analysis. In
addition, when monthly portfolio holdings were applied to a two index model, market timing
abilities were non-existence.
Comparing the use between stockholdings and trades, Chen, Jegadeesh and Wermers
(2000) reported that active stock trades represents a stronger opinion of a manager as
compared to a “passive” stockholding. Although no evidence of predictive abilities, Chen,
Jegadeesh and Wermers (2000) and Baker, Litov, Wachter and Wurgler (2010) found that
trade buys outperformed the trades they sell.
Using a different approach, researchers have also evaluated market timing abilities
simultaneously with stock selection abilities. Similar studies by Chang and Lewellen (1984)
and Chen and Stockum (1986) evaluated market timing and stock selection skills at the same
time using mutual fund returns. Chang and Lewellen (1984) proposed that there is a

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possibility that fund managers might exploit returns by engaging in effective “macro” market
timing activities as well as careful “micro” security selection efforts. However, there were no
evidence of market timing abilities. Following the same method, Kacperczyk, Niewerburgh
and Veldkamp (2014) also evaluated market timing and stock selection abilities
simultaneously. However they took into consideration of the changing economic trends like
the boom and recession periods. They conditioned the state of the economy and developed a
new method where more weightage is given to a fund manager’s market timing success
during recession periods and stock picking success during boom periods. Studying mutual
fund holdings, they found market timing abilities in both recession and boom periods.
We contribute to the literature in several ways. First, we examine the market timing
abilities of fund managers by evaluating their statistically significant trade proportions that
encompass beta, sentiment beta and momentum. Second, we investigate if fund manager
adjust their portfolios between positive and negative trade proportions in accordance to the
various market cycles. Unlike past researchers, we study the proportions of these trades as
they provide insights on the direction that a fund manager was pursuing. We expect a higher
proportion of positive trades when the market is bullish or in an expansion phase. In contrast,
we expect a higher proportion of negative trades when the market is bearish or undergoing a
recession period. Third, we show that although momentum trade proportions had the least
number of quarter observations, they exhibited the most significant results from our
correlation and regression analyses. We observed that positive momentum trade proportions
exhibited an inverse relationship with the momentum index during bullish market periods.
Although results were inconsistent to our expectations, an inverse relationship suggests that
the fund manager may have pursed a contrarian strategy.
To identify trades that are engaged in market timing in any calendar quarter, we
conducted correlation and regression analyses between the trade proportions and their related

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market indicators. We apply these measures to mutual fund trade proportions from 1991 to
2012. We conduct an overall correlation and regression test between the trade proportions
and their respective indicators to appreciate the general direction of their relationship. Next,
we consider how bullish and bearish markets will affect the adjustments of trade proportions.
We expect that during bullish market periods, the positive trade proportions would exhibit a
direct relationship with the market indicators. Similarly, during bearish market periods, we
expect negative trade proportions to exhibit a direct relationship with the market indicators.
Finally, various robust tests were also conducted to investigate if fund managers were
selective with the adjustments of their portfolio proportions based on market persistence,
turning points of the market and we study how big and small changes in the market returns
will affect their portfolio adjustment decisions.
We observe the following results from the correlation and regression analyses. Based
on the results of overall correlation and regression analysis, we observe that the positive
sentiment and positive momentum trade proportions exhibited significant results. However,
both trade proportions had an inverse relationship with their respective indicators. There were
no significant results from the beta trade proportions. Second, when bullish and bearish
market conditions are considered, the most number of significant results were exhibited from
the sentiment and momentum trade proportions. We observe an inverse relationship between
these trade proportions and their respective indicators. Third, based on the results from the
robust tests, the most number of significant results were also from the sentiment and
momentum trade proportions. Likewise, inverse relationships were exhibited between these
trade proportions and their respective indicators. Overall, despite momentum trade
proportions having the least number of quarter observations, they displayed the most number
of significant relationships. It is plausible that these fund managers have adopted a contrarian
strategy. Similar to previous findings, there we no evidence of market timing abilities.

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We consider some limitations of the study. Although the use of quarterly data
observations provides more time allowance for fund managers to form market expectations
and make right decisions in portfolio adjustments, these observations may not be able to
capture sufficient information of fund managers with higher trading frequencies. It is also
possible that the total number of quarter observations might have affected our results.
Therefore, we suggest some areas of future research. We consider evaluating a longer time
period that incorporates all four recession periods in future studies as research have shown
that predictability skills are best displayed during recession periods. We also suggest
evaluating market timing and stock selection skills simultaneously with regards to the
changes in the economic conditions using the trade proportions of mutual funds.
This paper is organized in the following manner. In Section 2.0, we discuss the
literature review. In Section 3.0, we discuss the data and provide an overview of the
methodology. In Section 4.0, we discuss and present our findings. In Section 5.0, we conduct
various robust tests. In Section 6.0, we conclude our study. An overview of our study’s
methodology is provided (Refer to Figure 1.1).

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Figure 1.1 Schematic Diagram: Overview of Methodology
The figure below illustrates the overview of our methodology. Trade betas that encompass beta,
sentiment beta and momentum are provided by Cullen et al. (2015). Quarterly data observations of
trade proportions are used for the analysis.
Indices
Trade Betas
(Proportions)
(1991-2012)
Trades associated with
Market Beta
Sentiment Beta
Momentum
S&P500 Market Index
Baker & Wurgler’s Sentiment Index
S&P 500 Momentum Index
S&P 500 Quality Index
S&P 500 Growth Index
S&P 500 Low Volatility Index
S&P 500 High Beta Index
Systemic risk
measures
Market Trends
-Bull and Bear Markets
-Recession and Boom Periods
-Further break down of Bull and Bear Markets
with the consideration of Volatility
Quarterly
Convert Data
Correlated
Check with
Daily
Monthly
Quarterly

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CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
This chapter describes the background and presents the literature review for this
study. In section 2.2, we discuss the background of our study and review the literature on the
key areas which are the market timing abilities of mutual funds managers, the evolution
performance measures which involves the use of stationarity and non-stationarity beta and the
examination of stockholdings and trades of mutual funds. We also identify the purpose of our
research and provide an overview of our methodology. Our sample comprises mainly of
statistically significant trade betas that encompass beta, sentiment beta and momentum of US
equity mutual funds over the period 1991 to 2012 and the data are provided by Cullen et al.
(2015).
2.2 Overview of Literature
Millions of people have invested in a once obscure financial instrument, the mutual
fund. Investors have constantly compared the advantages between active trading and passive
trading strategies of mutual funds. Over the years, the evaluation of mutual fund
performance has been vital to ensure optimal investment allocation as well as the
development of a mutual fund manager’s reward structure. Nevertheless, performance
measures have been consistently challenged and subsequently refined. Measures of a mutual
fund’s performance includes stock selection, market and industry timing abilities. Stock
selection and market timing abilities are the most popular measures of performance where
stock selection is the ability to select undervalued securities and market timing is the ability
to adjust security holdings to anticipate the movements of the market.

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Determining the market timing abilities of mutual fund managers have been the focal
point of research. Managers with market timing abilities can attempt to exploit returns using
two common strategies. They can either move in and out of the market or conduct a tactical
asset allocation between low and high beta stocks using predictive methods by monitoring the
performance of indicators like the S&P 500 Market Index to detect any changes in the market
trends. The early stages of determining market timing abilities was derived using a quadratic
term in the capital asset pricing model (CAPM). Subsequently, researchers had focused on
the stationarity and non-stationarity of beta in the bull and bear market. The increase
(decrease) of a non-stationarity fund’s beta allows the fund’s equity holdings to rebalance in
the anticipation of the expected bull (bear) market.
In order to avoid these benchmark issues, recent studies have concentrated on mutual
fund holdings and mutual fund trades. The intuition is that a fund with successful market
timing skills will hold more stocks that possess high beta in bull markets and conversely hold
predominately lower beta in bear markets. Similarly, a fund will purchase high beta stocks
and sell low beta stocks when the market is expected to rise and purchase low beta stocks and
sell high beta stocks when the market is expected to fall.
We contribute to the literature in several ways. First, we examine market timing
abilities of fund managers by evaluating the statistically significant trades that encompass
beta, sentiment beta and momentum. Trade proportions are used as they provide insights on
the direction that the fund manager is pursuing. Second, we consider both upmarket and
downmarket periods in our study. Third, using a new approach, we investigate if fund
managers make technical adjustments to their portfolios according to different market trends
based on market indices. During the bullish periods, we expect a higher proportions of
positive trades in a fund’s portfolio. On the other hand, during bearish markets, we expect a
higher proportions of negative trades in a fund’s portfolio. Market timing is significant when

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we observe a positive significant relationship between the market indices and the statistically
significant trade proportions.
This paper is organized in the following manner: in Section 2.0, we discuss the
literature of our research. Section 3.0, we provide and discuss the data and overview of the
methodology. In Section 4.0 we discuss the results of our research. Section 5.0, we conduct
various robust test. Finally in Section 5, we conclude the study and discuss about the
limitations of our study and suggest areas of future research.
2.3 Characteristics of Mutual Funds
Generally, in comparison to larger investment companies, individual investors lack of
substantial wealth to invest in large variety of stocks, bonds and securities. Consequently,
these individual investors turned into risk averse investors. Russell (2007) explained that
individual investors usually lack of professional knowledge and experience to make the best
decisions for their portfolios. Also, due to time management issues and complicated
paperwork, investors often struggle to keep up to their portfolios.
By offering diversification and simplicity for individual investors, mutual funds are a
good solution to these problems as they are a collection form of investments (Russell, 2007).
These funds are open-end investment companies and they pool funds of individual investors
offering them professional management by investing in a variety of securities or other assets
(Russell (2007); Bodie, Kane and Marcus (2014)). Instead of owning individual stocks or
bonds, mutual fund investors owns a portion of shares in a mutual fund and these shares
represent a portion of the holdings of the funds (Investopedia, 2016). The common types of
mutual funds are the money market funds, equity funds, bond funds, hedge funds and index
funds (Bodie, Kane and Marcus, 2014).

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Commonly used by sophisticated investors due to their numerous advantages, mutual
funds are also well known for their professional management of money (Investopedia, 2016).
As investors may lack time or expertise to manage their own portfolio, these funds offer
convenience and cost efficiency as they allow investors to have an inexpensive way to make
and monitor their investments (Investopedia, 2016). Bodie, Kane and Marcus (2004)
explained that as mutual funds includes a wide range of securities, this reduces portfolio risk
as any loss in a particular security can be minimised by the gains of others (Investopedia,
2016).
Mutual funds also offer lower transaction costs as they are usually purchased and sold
in large volumes of securities in bulk (Bodie, Kane and Marcus, 2004). Compared to
individual investors, these large scale investors are usually given a discounted trading cost.
Additionally, mutual funds are valuable for their liquidity advantages. Although they are a
collective form of investments, they allow shares to be converted into cash at any point of
time of request like an individual stock (Bodie, Kane and Marcus, 2004).
2.4 Mutual Fund Performance – Market Timing
In our study, market timing is the ability of a fund manager to adjust his or her
portfolio composition between high volatile stock and low volatile stocks based on using
predictive methods such as technical indicators like the market index. The market index
reflects the overall performance of the market and suggesting periods of bullish or bearish
market trends.
Fund managers that possess market timing abilities can generate superior returns by
adjusting their portfolios in accordance to the anticipated market trend. During bullish
(bearish) market periods, fund manager can adjust their portfolios towards high (low) volatile

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stocks. In other words, during bullish (bearish) periods, fund manager can exploit returns by
buying high (low) beta stocks and selling low (high) beta stocks.
2.4.1 Timing using Convex Relationship between Fund Returns and Market Returns
There has been an ongoing debate on the best performance measure for market timing
abilities of fund managers. Traditional performance measures like the Capital Asset Pricing
Model (CAPM) have reported that the relationship between the fund returns and market
returns are linear. Conversely, the study by Treynor and Mazuy (1966) showed that the
relationship between the fund returns and market returns are actually convex. Treynor and
Mazuy (1966) evaluated market timing abilities of mutual funds based on their historical
success in predicting major fluctuations in the stock market. They concluded that successful
market timers would increase their exposure to the market when a bullish period is
anticipated and reduce exposure to the market when a bearish period is anticipated. This
action causes the characteristic line of the portfolio to surpass the market as the portfolio
asset structure can be constantly adjusted (Figure 2.1).
Figure 2.1: The Characteristic Line of a Fund that Outguess the Market (Treynor and Mazuy,
1966)
Volatility
Volatility
Fund Returns
Market Returns
Characteristic Line

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Using the basis of the CAPM, Eq. (1), Treynor and Mazuy (1966) developed a least
square regression technique performance focusing on the squared relation between fund
returns and market returns. A curvature line was identified by fitting in the characteristic line
data of 57 open-end mutual funds using their yearly data observations of returns during the
period between 1953 and 1963, Eq. (2):
𝑅𝑖𝑡 = 𝑅𝑓 + 𝛽𝑖(𝑅 𝑚𝑡 − 𝑅𝑓𝑡) (1)
𝑅𝑖𝑡 − 𝑅𝑓𝑡 = 𝑎𝑖 + 𝛽𝑖(𝑅 𝑚𝑡 − 𝑅𝑓𝑡) + 𝛾𝑖(𝑅 𝑚 − 𝑅𝑓𝑡)
2
+ ℯ𝑖𝑡, (2)
where, 𝑅𝑖𝑡 denotes return on assets of the selected fund at time t, 𝑅𝑓𝑡 denotes risk free return
rate at time t, 𝑅 𝑚𝑡 is the return on the market at time t, 𝑎𝑖 denotes a selectivity ability,
𝛾𝑖 denotes the parameter measuring the market timing performance, if 𝛾𝑖 > 0, it implies the
existence of a timing ability. The difference between the equation of the CAPM model and
the Treynor and Mazuy model is the addition of 𝛾𝑖(𝑅 𝑚 − 𝑅𝑓𝑡)
2
as this changes the linear
relationship between the fund returns and market returns into a quadratic equation.
Treynor and Mazuy used yearly data observations of returns as they believed that
even for smaller funds, the frequency of portfolio changes which will alter their fund’s
volatility will not happen more than once a year. However, only one out of 57 funds exhibited
a curve characteristic line. This suggest that on average, mutual funds were not successful at
outguessing the market. Treynor and Mazuy (1966) concluded that any excess returns
generated were not from the success of timing abilities but from the abilities of fund
managers in identifying under-priced industries and companies.
Supporting the study of Treynor and Mazuy (1966), Williamson (1972) stated that the
relationship between the fund returns and the market returns would be convex instead of
linear. Based on the characteristic line graph, when the line is curved upwards at the upper

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right and lower left end, this suggest that mutual funds had performed well in bearish markets
and performed even better during bullish markets.
Williamson (1972) attempted to identify market timing abilities by reviewing the
available published data of 180 mutual funds during the period between 1961 and 1970.
However, similar to Treynor and Mazuy (1966), on average, no mutual funds were able to
outperform the market. Moreover, four out of 180 funds displayed significant unsuccessful
forecasting. Against expectations, these funds were more volatile during bearish market and
less volatile during bullish markets.
2.4.2 Stationary Beta versus Non-Stationary Beta in Bull and Bear Markets
Jensen (1968) believed that the performance of risky investment portfolios is the
ability of a portfolio manager to earn superior returns through successful predictions of future
security prices. These returns should be higher than the returns expected by the portfolio
manager for the level of risk associated with their portfolios. This belief is based on the
concept that on average, the riskier the asset is, the higher the returns will be. Portfolio
managers will be compensated for taking on additional risk. If the asset’s actual returns are
above the expected returns of the asset, a positive alpha is established.
On the contrary to earlier studies that evaluated forecasting abilities of portfolio
managers using relative performance measures, Jensen (1968) has provided an absolute
measure of performance. Absolute performance measure is a measure that is compared
against a certain standard. The Jensen’s equation determines the superior returns obtained
when deviated from the benchmark, Eq. (3):
𝛼𝑖 = [𝑒(𝑟𝑖𝑡) − 𝑟𝐹𝑡] − 𝛽𝑖(𝑒(𝑟 𝑚𝑡) − 𝑟𝑓 𝑡), (3)

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where, 𝑟𝑖𝑡 denotes the return of fund 𝑖 at time t, 𝛼𝑖 denotes the abnormal returns of the fund
(an idea of forecasting abilities), 𝛽𝑖 denotes the systematic risk of the fund 𝑖, 𝑟 𝑚𝑡 denotes the
return of the market at time t and 𝑟𝑓 𝑡 denotes the risk free rate at time t. The value of alpha
could either positive or negative. Having a positive alpha would imply superior forecasting
abilities and in contrast, having a negative alpha would imply either poor selection choices or
the existence of high expenses.
Given that the predictability skills of a portfolio manager not only involves the skills
to predict price movements of individual securities and the general behaviour of future
security prices, the Jensen (1968) model also considers the abilities of a fund manager to
forecast the market behaviour. Henceforth, the Jensen (1968) model not only evaluates the
portfolio manager’s ability to predict how much a security or portfolio is expected to earn
given the level of systemic risk (measured by beta) but also measures the ability of a portfolio
manager to forecast the market’s behaviour. However, this is based on the assumption that
the portfolio manager tries to maintain the given level of risk in his or her portfolio.
Jensen (1968) investigated the existence of predictability skills by analysing 115 open
ended mutual funds using their yearly data observation of returns during the period between
1945 and 1964. Based on the results, on average, mutual funds were not able to predict
security prices to outguess the market henceforth underperforming buy and hold strategies.
They were also unsuccessful in their trading activities to recoup brokerage expenses. We
consider some limitations of this study. The assumption that the portfolio manager attempts
to maintain the same level of risk may have caused inaccurate results. As mutual funds are
being actively managed, it is reasonable to expect changes in the level of risk due to the
buying and selling decisions of portfolio managers.

27
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Subsequently, there has been attention drawn to the stability of the systematic risk
measured by beta in bull and bear market conditions. The systematic risk of mutual funds in
different market conditions is an important factor in evaluating the market timing abilities of
a fund manager. If there is different beta for different market conditions, using a stationary
beta for the entire period can result in different conclusion of a fund manager’s abilities.
During market changes, when a stationary beta is used for the entire time period there is no
consideration for the additional risk exposure. If a fund manager correctly adjusts the fund’s
beta in an anticipation of a bull market, the beta in a bull market would be greater than the
estimation of beta for both bull and bear market period. One of the limitations from Jensen
(1968) study was the use a stationary beta for the entire period of the study as the fund
managers attempted to on average, maintain the given level of risk in their portfolio.
Taking into consideration a non-stationary beta, Fabozzi and Francis (1979)
investigated if the beta of mutual funds varies in bullish and bearish markets. A statistical
model was developed by Fabozzi and Francis (1979) to examine if the systemic risk of
mutual funds was altered during different market conditions. The monthly data observations
of returns of 85 mutual funds were tested between the period from 1965 and 1971. In order to
examine if the systematic risk (beta) are different in various market conditions, this equation
has taken into consideration of beta shifting, Eq. (3):
𝑅𝑖 = 𝐴1𝑖 + 𝐴2𝑖 𝐷𝑡 + 𝛽1𝑖 𝑅 𝑚𝑡 + 𝛽2𝑖 𝐷𝑡 𝑅 𝑚𝑡 + ℯ𝑖𝑡, (4)
where, 𝑅𝑖 denotes the excess returns of fund i, 𝑅 𝑚𝑡 denotes the excess returns on the market
𝐷𝑡 denotes a dummy variable which is unity if the tth period is a bull market and zero
otherwise, The coefficients of the dummy variable, 𝐴2𝑖 and 𝐵2𝑖, measure the differential
effects of bull market conditions on the alpha, 𝐴1𝑖 and beta, 𝐵1𝑖 respectively and ℯ𝑖𝑡 is the

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random error term. This equation allows the shifting of alpha and beta and is designed to
determine if the regression coefficients are significantly different in bull and bear markets.
Fabozzi and Francis (1979) reported that there were three definitions of bull and bear
markets for this study. First, defined by a well-established textbook (Cohen, Zingbarg and
Zeikel, 1973), certain months were designated as bull and bear markets in accordance to
market trends. Second, when market returns positive, the market is known to be bullish.
When market returns are negative, the market is known to be bearish. Third, without the
consideration of market trends, months with market returns higher (lower) than one half of
the standard deviation of market returns over the sample period are designated as bull (bear)
markets.
While betas of individual securities may be stable despite changes in market trends
like the bull and bear markets, Fabozzi and Francis (1979) argued that there is a possibility
for a non-stationary beta to occur even if the fund manager did not attempt to adjust the
portfolio risk. They considered how the individual securities’ betas may be intertemporally
unstable. Also, changes in the relative market value weights of individual securities will alter
the portfolio’s beta, which is the weighted average beta regardless if the betas of individual
securities were not altered. Therefore, a benchmark is created to determine if the number of
funds that shifted in beta were a result of a planned changed in risk exposure. For comparison
purpose, 85 random portfolios were created as benchmarks. Each stock of the 85 random
portfolios were given equal weightage.
Despite considering a non-stationary beta, results suggest that regardless of different
market conditions, on average, mutual funds did not respond differently. Similar to previous
studies, mutual fund managers were not able to outguess the market to earn higher risk-

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adjusted returns for shareholders. Fund managers did not alter their fund’s beta to benefit
from the different market conditions.
Fabozzi and Francis (1979) revealed three reasons why fund managers were not
observed to increase their funds’ beta during bearish to bullish market periods or decrease
their funds’ beta during bullish to bearish periods. One, there were random beta coefficients
from a significant number of New York Stock Exchange (NYSE) stocks and the portfolio
managers might have overvalued or undervalued the beta. Two, there is a possibility that the
portfolio manager was unable to foresee changes in market conditions hence was unable to
shift the fund’s beta during bullish markets. Three, although fund managers may have
correctly anticipated the right change in direction of the market, the cost of altering a fund’s
beta may not be justifiable for the gains in return.
An extension to the Fabozzi and Francis (1979) study, Kim and Zumwalt (1979)
investigated if there were variations of returns of securities and portfolios in up (bull) and
down (bear) markets. This process has the effect of separating the total variation of the
security or portfolio returns into two components, variations when the market is up and
variations when the market is down. Kim and Zumwalt (1979) pointed out that although the
beta of mutual funds are not significantly different in up and down market periods, the
variations of returns of mutual funds may be different. If investors are presumed to be risk-
averse, they would expect to receive a premium for bearing additional risk from the “down”
market and expected to pay a premium for the returns they would receive from the “up”
market.
For the development of the study, two assumptions were employed. The first
assumption was that each security may react differently in up and down markets. If securities
do respond differently, beta coefficients may be determined for both up and down markets

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Quarterly Trades
and investigated for statistically significant differences. There were three measures to
determine what establishes an “up” or “down” market. An “up” market are months with rate
of returns on the market portfolios that exceed the 1) average market return, 2) the risk free
rate or 3) zero. Otherwise, the market is defined as a “down” market. The single index model
was modified to examine both up and down betas, Eq. (5):
𝑅𝑖𝑡 =∝𝑖+ 𝛽𝑖
+
𝑅 𝑚𝑡
+
+ 𝛽𝑖
−
𝑅 𝑚𝑡
−
+ ℯ𝑖𝑡, (5)
where, 𝑅𝑖𝑡 denotes the excess return of fund i , ∝𝑖 denotes the actual return of fund i minus
the expected return of fund i, 𝑅 𝑚𝑡 denotes the excess return on the market, ℯ𝑖𝑡 is the random
error term, 𝛽𝑖
+
is determined from the months when the returns comes from the “up” market
and 𝛽𝑖
−
is determined when the returns come from the “down” market. As the number of
securities in the portfolio increases, the unsystematic risk also known as firm-specific risk
would be diversified away. The variance of portfolio equation would be written as, Eq. (6):
𝜎 𝑝
2
= (𝛽 𝑝
+
)2
𝜎 𝑝
2
𝑝+ + (𝛽 𝑝
−
)2
𝜎𝑝
2
𝑝−, (6)
where, 𝜎 𝑝
2
is the variance of the portfolio. The formula is separated into (𝛽 𝑝
+
)2
𝜎𝑝
2
𝑝+ being the
variations from the bull market and (𝛽 𝑝
−
)2
𝜎𝑝
2
𝑝− being the variations from the bear market.
The second assumption was that investors had a preference for greater up side
variation of returns and a preference for a smaller downside variation of returns. This
suggests that an investor has a preference that is positively related to the upside variations
and negatively related to the downside variations. Kim and Zumwalt (1979) believed that
investors require a risk premium on the downside portion of variation and a negative risk
premium on the upside portion of the variation. Expressed in Eq. (7):
𝐸(𝑅 𝑝) = 𝑅𝑓 + 𝜆1 𝛽 𝑝
+
+ 𝜆2 𝛽 𝑝
−
(7)

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where, 𝜆1 𝛽 𝑝
+
denotes the negative coefficient and 𝜆2 𝛽 𝑝
−
denotes the positive coefficient. The
two beta model of equation 7 was tested to determine if the expected negative value for 𝜆1
and positive value for 𝜆2 was confirmed.
Kim and Zumwalt (1979) developed the two beta model to incorporate the responses
of beta during “up” and “down” market periods. The variations of returns in both market
periods were investigated using the monthly data observations of returns from a sample of
322 securities between the periods from 1962 to 1976. This model allows the separation of
the total systemic risk into two components, risk from upside variations markets which are
considered to be favourable and risk from bearish markets which are considered to be
unfavourable.
Results reflected that out of 322 securities, 34 exhibited significantly different up and
down market betas. In comparison to the Fabozzi and Francis (1979) study, more securities
displayed statistically significant differences between “up” market and “down” market betas
than would occur randomly. The signs of the regression coefficients were also correct and
statistically significant, suggesting that investors do receive a risk premium for tolerating
downside risk. Consistent to Kim and Zumwalt’s expectations, the negative premium was
associated to the beta of the “up” market. This suggests that the measurement of downside
variation of returns is more appropriate when measured by the “down” market beta rather
than the conventional single beta in the market model.
Miller and Gressis (1980) created a new measure based on the traditional CAPM
which allows and statistically estimates the extent of non-stationarity in the relationships
between the fund returns and market returns. This measure allows a precise estimation of
alpha and beta in the presence of non-stationarity beta. Miller and Gressis (1980) revealed
that if non-stationarity is significant in a risk return relationship but is ignored, this can result

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in misleading information as the estimates of alphas and betas are calculated based on a
stationary beta which is the weighted averages of the actual values. When mutual funds are
actively managed, the level of systemic risk would fluctuate as a result of the buying and
selling decisions of their managers. Hence beta, the measure of systematic risk should not be
ignored as this might result in biased results. It is also reasonable to expect non-stationary
risk return relationships in some mutual funds as well-managed funds would take advantage
of the market by altering their betas in accordance to the general market movements.
Miller and Gressis’s (1980) approach is based on the traditional CAPM which allows
and statistically gauge the extent of non-stationarity in relationships between the returns of
funds and the returns from the market. In order to obtain a more precise estimate of beta and
alpha, time can be segmented into intervals during which the betas are stationary. A
partitioning algorithm and partition selection procedure is conducted on the sample of 28
mutual funds using the weekly data observations of returns between the periods from 1973 to
1974. Unlike previous researchers that evaluated the performance of mutual funds using
yearly or monthly data observations of returns (Jensen, 1986; Fabozzi and Francis, 1979;
Kim and Zumwalt, 1979), Miller and Gressis (1980) used weekly data observations of returns
as they believed that it is a more appropriate measure in detecting shifts between the risk and
returns of mutual funds.
The presence of a non-stationary beta would suggest either changes in the distribution
of risk in the economy or changes in the mutual fund portfolio composition. Investors are
interested in such changes as they attempt to take advantage of these deviations to earn
superior returns. Based on the results, only one out of 28 funds exhibited stationary betas and
the rest had betas that varied over the periods. Based on correlation and regression analysis
results of the information gathered from the partition regression, a mixture of results were
exhibited between the betas and the market returns. There were some evidence of weak

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positive relationships and some weak negative relationships between the betas and market
returns. Similarly, there were also weak negative relationships and weak positive
relationships between the alphas and betas. However, there were no statistically significant
relationships of either type.
Following up on the studies that incorporated the use of non-stationary beta, Chen
(1982) re-examined the relationship between the risk and returns of mutual funds in in bull
and bear market conditions. Chen (1982) evaluates the study of Kim and Zumwalt (1979) as
their procedure of valuing “up” and “down” market betas may have led to in inaccurate
results in the risk analysis of “up” and “down” markets.
Chen (1982) revealed that the study by Kim and Zumwalt (1979) gave inconsistent
results due to multicollinearity issues which resulted in large sampling variances of estimates
of the “up” and “down” market betas. Also, the model did not take into consideration that the
beta coefficient would change over time. Chen (1982) used a time-varying beta coefficient
approach to resolve these issues. It is revealed that the two beta model used for the test of the
trade-off between the risk and returns in “up” and “down” market is constant regardless of a
stable or non-stable beta coefficient. The two beta model from the Kim and Zumwalt’s study
was modified to be, Eq. (8):
𝐸(𝑅 𝑝𝑡) = 𝑅𝑓 + 𝛽 𝑝
+
𝐸(𝑅 𝑚𝑡 − 𝑅𝑓)
+
+ 𝛽 𝑝
−
𝐸(𝑅 𝑚𝑡 − 𝑅𝑓)
−
+ ℯ𝑖𝑡 (8)
where, 𝐸(𝑅 𝑝𝑡) denotes the expected return of the portfolio, 𝑅𝑓 denotes the risk free rate of
interest, 𝛽 𝑝
+
denotes the bull market beta, 𝛽 𝑝
−
denotes the bear market beta and ℯ𝑖𝑡 denotes the
random error term.
The sample of 360 mutual funds’ monthly data observations of returns were tested
between the periods from 1965 to 1977. Similar to Kim and Zumwalt’s (1979) results, Chen

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(1982) concluded that investors do require a premium for taking on risk from the downside
market and investors pay a premium for the returns they receive from the “up” market. The
results of the time varying beta approach method have supported the Kim and Zumwalt’s
findings that the breakdown of total systemic risk into risk due to upside deviation of returns
and risk due to the response of a bear market still appeared to be correct even with a non-
stationary beta. Irrespective of a stationary or non-stationary beta, investors do request
compensation for undertaking the risk from the variation of returns from the bear market
which was viewed as unfavourable and pay a premium for the upside variation of returns
which was viewed as favourable.
Both studies by Chen (1982) and Kim and Zumwalt (1979) revealed that an
appropriate measure of downside risk (bear market) would be the “down” market beta instead
of a stationary beta. It is not appropriate to consider the use of a stationary beta as a
measurement of the market as the market cycle changes over time. A stationary beta does not
give any allowance for the increase in risk exposure.
2.4.3 Evaluating Market Timing Abilities simultaneously with Security Selection
Abilities
Past research have investigated the market timing abilities of fund managers
individually. Using a different approach, Chang and Lewellen (1984) evaluated market
timing abilities of fund managers simultaneously with security selection abilities. They
believed that portfolio managers might be able to exploit returns by engaging in effective
“macro” market timing activities as well as cautious “micro” security selection efforts. That
is the ability to modify the total risk composition of their portfolios in the anticipation of the
general movements of the market. This study considers the fact that a non-stationary beta
would be a more appropriate measure of mutual fund performance. Based on the studies by

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Fabozzi and Francis (1979), Kim and Zumwalt (1979), Miller and Gressis (1980) and Chen
(1982), they found some evidence that mutual fund portfolios do not have a constant risk
position over time. They also concluded that skills of market timing may well be a
measurement of a fund manager’s decision process.
Chang and Lewellen (1984) conducted a parametric statistical procedure that allowed
a joint test for the presence of either security selection or superior market timing skills in
managed portfolio to investigate the performance of 67 mutual funds using their monthly data
observations of returns between the periods from 1971 to 1979. Majority of research have
evaluated the performance of mutual funds based on the single market model equation, Eq.
(9):
𝑍 𝑝(𝑡) − 𝑅(𝑡) = 𝑎 𝑝 + 𝛽 𝑝[𝑍 𝑚(𝑡) − 𝑅(𝑡)] + 𝜖(𝑡), (9)
where, 𝑍 𝑝(𝑡) denotes the observed rate of return on the portfolio p during the period, 𝑅(𝑡)
denotes the simultaneous rate of return on a riskless asset, 𝑍 𝑚(𝑡) denotes the return on the
fully diversified “market” portfolio of all risky assets during t and 𝜖(𝑡) denotes the random
error term with it being a value of 0. 𝛽 𝑝 is assumed to be stationary over time. When alpha
has a positive value, this indicates superior return performance based on security selection
efforts. However, this model only evaluates stock selection abilities and does not take into
consideration that the level of systemic risk (𝛽 𝑝) might change over time.
The equation was later modified by Henriksson and Merton (1981) to a least square
regression which evaluates the stock selectivity and market timing abilities of mutual fund
abilities separately. It was also modified to capture an “up-market beta” and a “down-market
beta.” The modified equation was, Eq. (10):
𝑍 𝑝(𝑡) − 𝑅(𝑡) = 𝛼∗
+ 𝛽1
∗
𝑋1(𝑡) + 𝛽2
∗
𝑋2(𝑡) + 𝜖 𝑝
∗(𝑡), (10)

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where, 𝑋1(𝑡) and 𝑋2(𝑡) =𝑍 𝑚(𝑡) − 𝑅(𝑡), 𝛽1
∗
denotes the “up-market beta” of a managed
portfolio and 𝛽2
∗
denotes the “down market beta”. The contributions of returns have been
separated into two components, α represents the returns due to security selection ability and
𝛽 is used to measure the portfolio’s market-timing skill. When there is no existence of market
timing abilities, the value of beta would be zero.
While this was a joint test that considered both market timing and stock selection
abilities of fund managers, results suggest that on average, neither skilful market timing nor
clever security selection abilities were evident. Overall, mutual funds were unable to outguess
the market. It seemed that passive strategies still have an upper hand in mutual fund
investments.
Similar to Chang and Lewellen (1984), Chen and Stockum (986) also investigated the
market timing and stock selection abilities of fund managers simultaneously. Traditional
performance measures like the Sharpe ratio assumed that that the systematic risk level of a
fund is a fixed coefficient rather than a decision variable. However, this results in inaccurate
performance measures as the risk of the portfolio varies over time. Following which, studies
have incorporated the use of a non-stationary beta. However, they did not consider that the
mutual fund’s beta could also be non-stationary when fund managers are not engaged in
timing decisions (Fabozzi and Francis, 1979; Kim and Zumwalt, 1979; Miller and Gressis,
1980; Chen, 1982). Hence, the presence of a non-stationary beta does not necessary represent
the existence of market timing abilities.
Chen and Stockum (1986) presented a generalized varying parameter model to
examine the performance of mutual funds by allowing for both timing decisions of funds and
random behaviour of fund’s systematic risk levels. Although the generalized varying

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parameter model is similar to the Treynor and Mazuy model, this model allows the beta of
mutual funds to be a decision variable instead of a fixed coefficient, Eq. (11):
𝑅𝑖𝑡 = 𝑎𝑖 + 𝑅 𝑚𝛽
−
+ 𝜆𝑖 𝑅 𝑚𝑖
2
+ 𝜔𝑖𝑡 (11)
where, 𝜔𝑖𝑡 equals (𝜇𝑖𝑡 + 𝜖𝑖𝑡 𝑅 𝑚𝑡). 𝑅𝑖𝑡 denotes mutual fund i’s return at time t, 𝑅 𝑚 denotes the
market return at time t, 𝜇𝑖𝑡 denotes random shock, β denotes target systemic risk, 𝑎𝑖 measures
the selectivity component and 𝜆𝑖 𝑅 𝑚𝑖
2
measures changes due to market timing. A portfolio
beta might still be non-stationary even if fund managers are not actively managing their
portfolios by adjusting the portfolio beta in accordance to the market. This is because a
portfolio beta might respond differently to various market cycles.
Chen and Stockum (1986) examined 43 mutual funds using their quarterly data
observations of returns between the periods from 1975 to 1982. Unlike prior studies that used
monthly or yearly data observations of returns, Chen and Stockum (1986) stated that the use
of quarterly data observations gives fund managers an extended period of time to form
market expectations and adjust their portfolios accordingly. Throughout this sample period,
there were two bull and two bear market periods. By incorporating both cycles of the
markets, it will help to reduce biasness in this study.
Based on the results, 30% of funds showed selectivity, 19% were random betas and
14% showed significant but negative market timing performance. Although there were some
significant selectivity abilities, results suggest that similar to previous findings, mutual funds
did not reflect any market timing abilities regardless individually or as a group.
2.4.4 Free from Beta Estimates
On the contrary to prior research, Ferri, Oberhelman and Roenfeldt (1984) examined
the market timing abilities of mutual funds without the use of beta estimates. This method

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Quarterly Trades
focuses on the composition of assets in a fund’s portfolio and investigates alterations in the
composition prior to variations in the broad level of stock market prices. The main objective
of this method is to examine whether a fund manager’s decision to gradually increase or
decrease the fund’s commitment to common stocks. The expectations of fund managers are
reflected on the decisions that they made and simultaneously shifts the portfolio’s market-
related volatility.
A fund manager is successful at market timing when their decisions and expectations
are consistent with the later movements of the market. For instance, a fund manager who
anticipates a bearish market will lower the portfolio’s volatility by decreasing the percentage
of assets in a portfolio that are invested in stocks. Market timing skills are exhibited if the
later market is bearish. Likewise, successful market timing is exhibited when a fund manager
increases the portfolio assets invested in stocks in the expectations of an increase in market
prices and the later market is bullish.
Ferri, Oberhelman and Roenfeldt (1984) examined the quarterly changes in the
mutual fund’s stock holdings of 69 mutual funds between the periods from 1975 to 1980.
These types of mutual funds have aggressive management with a preference of being
completely invested by stocks. Therefore, any alterations in these funds are considered as an
attempt to forecast or time the market movements. Additionally, two subgroups of
stockholdings were also examined, those preceding extensive fluctuations in stock prices and
those when managerial reallocations of portfolios are not impacted by shareholder’s
contributions or withdrawal from funds.
Market timing abilities are evaluated by examining the increases and decreases in a
fund’s relative commitment to stocks measured by the ratio of net purchases or sales of
common stocks (NETPS) to total assets. If the NETPS has a positive value, the fund has

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Quarterly Trades
purchased more stock than it sold, increasing its exposure to market risk. A t test was
conducted to compare the mean levels of NETPS for a fund quarter before an increase in the
stock index with the mean NETPS for the fund quarter before a decrease in the stock index.
Ferri, Oberhelman and Roenfeldt (1984) hypothesized that the NETPS is classified as an
upmarket decision if the stock index increases during the subsequent months and the NETPS
is classified as downmarket decision if the stock index decreases during the subsequent
months. The null hypothesis is rejected if the average NETPS for the upmarket is
significantly larger than the average NETPS for the down market. However, the test of means
could be inaccurate as there is a possibility that a fund made merely a few large mistakes as
the test results are reliant on the extent of the deviations in stock holdings. Therefore, a
frequency test is also conducted as it only examines the direction of changes in stock
holdings prior to the movements in the stock index and eliminates the limitations of the test
of means. A correct decision can either be classified as a positive NETPS before a bullish
market or a negative NETPS before a bearish market.
Based on the results, although a few funds displayed some market timing abilities, on
average, there were no significant market timing abilities exhibited. In sum, although this
study offers an alternative way of examining market timing abilities which is a method that is
free from the estimates of beta, there were no new evidence that fund managers possess
market timing abilities.
2.4.5 Portfolio Performance Measures without Benchmarks
Past researchers have evaluated performance measures of mutual funds by comparing
the returns of managed portfolios to the returns of a benchmark portfolio. However, this
could be a bias measure of market timing abilities as results are dependent on the choice of
benchmark selected. Often, information regarding the portfolio composition of funds are not

40
Quarterly Trades
utilised. Grinblatt and Titman (1993) believed that by making use of information about the
portfolio composition, the method of comparing returns to a benchmark portfolio can be
eliminated.
This method is adapted by the Event Study Measure where the performance of mutual
funds are evaluated by calculating the differences between the returns of assets during the
portfolio period known as “the event period” and returns of a later date known as “the
comparison period”. This is the belief that the assets held in a well-managed portfolio (event
period) would have higher returns compared to periods when assets are not included in any
portfolios (comparison period). This method uses later period returns compared to earlier
period returns as they have taken into account that some portfolio managers are likely to pick
their assets based on their past returns. However, this might be a bias assumption as it forces
the researcher to ignore assets that lacked returns in the comparison periods.
Grinblatt and Titman (1993) developed a new measure that is not subjected to
survivorship biases. It is based on the assumption that from the standpoint of uninformed
investors, the direction of expected asset returns is constant over time. This implies that the
portfolio holdings of an uninformed investor does not have any form of relationship with the
future returns. Unlike a well-informed manager who is able to predict when certain assets
will exhibit higher or lower than average returns, the direction of the expected asset returns
will vary over time. The manager can take advantage of these changing expected returns by
tilting his or her portfolio weights towards assets that have increased in expected returns and
tilt away from assets that have decreased in expected returns.
Grinblatt and Titman (1993) examined 155 mutual funds quarterly changes in
stockholdings from the period between 1974 and 1984. Concluding results showed that on
average, mutual fund portfolios exhibited positive abnormal investment performances and

41
Quarterly Trades
that the strongest performance was computed from the aggressive growth category of funds
which earned significantly positive risk adjusted returns. In relation to the study by Ferri,
Oberhelman and Roenfeldt (1984), any movements in these funds are considered as an
attempt to forecast or time the market movements. Although no market timing abilities were
present, this article emphasized that superior performance can be predicted without the use of
a benchmark when portfolio holdings were examined.
2.4.6 Volatility Timing
Previous studies have examined the market timing abilities of mutual fund managers
exclusively by comparing the returns between their funds and the market (Treynor and
Mazuy, 1966; Jensen, 1968; Fabozzi and Francis, 1979; Kim and Zumwalt, 1979; Miller and
Gressis, 1980; Chen, 1982). The main theory behind these studies often investigate if fund
managers have taken advantage of superior information by adjusting their funds towards
more (less) volatile stocks in the anticipation of bull (bear).
Often, fund managers encounter difficulties in predicting market returns. Using a new
perspective, Busse (1999) investigated the funds’ ability to time market volatility. He
examined if funds change market exposure in relation to market volatility changes and
highlighted that volatility timing is a significant influence in the returns of mutual funds as it
leads to higher risk-adjusted returns.
Attention has been shifted to market volatility for two reason. First, unlike market
returns which are hard to predict, market volatility is predictable because it is persistent. High
volatility is usually followed by high volatility and low volatility is usually followed by low
volatility. Second, majority of performance measures are risk adjusted. These measures affect
the cash flows of funds and how funds manage risk has repercussions for manager
compensation. However, it is uncertain that a fund manager can increase risk adjusted

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Quarterly Trades
performance or investor utility by timing market volatility. Therefore, Busse (1999)
investigates if funds respond to changes in market volatility and how these strategies will
affect the performance of funds.
Busse (1999) motivated volatility timing in the perspective of a fund manager,
assuming that fund managers attempt to time market exposure in the best interest of the fund
shareholder. Busse (1999) analysed the daily data observation of returns of 230 domestic
equity funds between 1985 and 1995 with a daily single factor volatility timing model to
study how managers respond to publicly available information. This single factor volatility
timing model is modified from the four index model by adding in terms to capture the effects
of volatility timing. Unlike previous researchers that analysed monthly return data (Fabozzi
and Francis, 1979, Kim and Zumwalt, 1979; Chen 1982; Chang and Lewellen, 1984), the use
of daily returns’ data allows a more efficient estimate of time variations in systematic risk
considering that monthly returns’ data might not be able to capture the day to day activities of
active mutual funds.
A conditional analysis was conducted to provide detailed explanations of mutual fund
risk and the reasons for its changes. It also allows the evaluator to differentiate between
passive effects and the effects by public information usage. Furthermore, it also helps to
differentiate among active managers of different abilities and as such lead to better asset
allocation decisions.
Based on the results, Busse (1999) found a strong inverse relationship between the
funds’ systemic risk levels and conditional market volatility. When conditional market
volatility is higher than average, systemic risk levels are lower. When conditional market
volatility is high, funds that reduce systemic risk earned higher risk-adjusted returns. This
demonstrates that mutual funds have taken advantage of the superior information by

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Quarterly Trades
increasing their market exposure when there is low market volatility and decreasing their
market exposure when market volatility is high.
2.4.7 Downside of Returns Chasing Behaviour
Over the years, researchers have focused on exploiting mutual fund returns. A
contrasting article by Karceski (2002) developed an agency model to express that such return
chasing behaviour of fund managers will lead to beta being under-priced to the degree that is
predicted by the standard CAPM. Based on Karceski’s (2002) model, he revealed that the
goals of fund managers and the behaviour of return chasing fund managers will influence
fund management to adjust their portfolios towards high beta stocks. Based on the theory of
supply and demand, this will lead to a high demand of high beta stocks which lead to an
increase in prices and in turn lower the expected returns. The model is supported by three
verifiable facts. First, investors tend to buy funds that have recently displayed extraordinary
returns. Second, fund managers chase returns through time. During the transition period from
the bear to the bull market, there is a tendency of larger cash inflows into the equity mutual
fund industry. Third, during bullish market periods, high beta stocks outperforms low beta
stocks.
Karceski (2002) believed that active fund managers focus on outperforming peers
during the transition phase between bearish and bullish markets as returns are usually larger.
The rewards from the bullish market are usually higher than the rewards from the bearish
market as cash inflows are usually minimised after a “down” market. Mutual fund investors
would tilt their portfolios towards high beta stocks during an upward market in anticipation
that it will lead to a larger cash inflow as high beta stocks typically outperform in bull
markets. However, this return chasing behaviour by mutual fund managers will lead to

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Quarterly Trades
CAPM’s beta to be either under-priced or overpriced than the expected amount, resulting in a
reduction of beta risk premium in equilibrium. In an equilibrium world, the demand for high
beta stocks prior the bull market will push their price higher and expected returns lower.
Karceski (2002)’s model predict that these actions will cause expected returns to fall.
The model investigates the monthly holdings of mutual funds from the period between 1984
and 1996. Consistent to Karceski (2002) expectations, results reflected that fund investors
appeared to be inexperienced as they tend to increase their equity funds stake after the market
goes up and pick funds based on their past performance despite justified warnings by
disclaimers to the contrary. Based on the agency model created, the behaviour of mutual fund
managers chasing returns across funds cause them to tilt towards high beta stocks resulting in
a flatter security market line and as a result reduces the expected returns premium for high
beta stocks. The total stock portfolio was over weighted with aggressive growth funds (high
beta stocks) compared to income equity funds (low beta stocks).
Results reflected that equity mutual funds held a larger percentage of high beta stocks
compared to the overall equity market portfolio. Karceski (2002) predictions were right that
due to active fund managers tilting towards high beta stocks, this reduces the expected return
premium for high beta stocks and flattens the security market line. In some extreme cases,
this may lead to the returns of low beta stocks surpassing the equilibrium expected returns of
high beta stocks despite conventional risk measures such as beta or standard deviation and
performance in bear markets suggesting that high beta stocks should acquire a higher
expected return.
2.4.8 Persistence in Fund Performance
Bollen and Busse (2005) believed that superior information is built on the expectation
that some fund managers possess significant predictive abilities and if this ability persists, it

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Quarterly Trades
allows vigilant investors to predict future performance based on past results. Bollen and
Busse (2005) examined the persistence in mutual fund performance emphasizing on short
term periods.
There are two types of decision making strategies, stock selection and market timing.
Stock selection refers to predicting returns of individual stocks and market timing refers to
predicting relative returns of broad asset classes. Majority of past studies found no significant
evidence that fund managers were able to achieve abnormal returns over long periods
regardless pursuing a stock selection or market timing strategies.
Bollen and Busse (2005) examined if mutual fund performance persist over a
relatively short period of three months. They reported that fund performance exists for a
relatively short amount of time due to the mutual fund industry being competitive by nature
or to managerial turnover. Having short measurement periods provides a more accurate way
of identifying top performers. Daily fund returns are examined with quarterly measurement
periods as Bollen and Busse (2005) argued that monthly fund returns will not be an efficient
estimation. Previous studies that used mostly monthly data of returns found insignificant
evidence that fund managers were able to generate positive abnormal returns from stock
selection abilities or market timing abilities over a long period of time. Also, using quarterly
measurement periods controls for cash flows as it allows mutual fund factor loadings to
gradually alter.
The parameters of stock selection and market timing models were estimated and stock
selection and market timing abilities were examined using the four factor model and two
timing models. Bollen and Busse (2005) allowed the coexistence of both types of abilities in
their measurement as previous studies have focus on either one of this abilities individually
without taking into consideration that some fund managers are stock pickers whereas some

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Quarterly Trades
are market timers. By stereotyping them as one type of fund manager could result in
inaccurate results. There is a likelihood that fund managers may also switch strategies. They
studied the daily returns of 230 funds between 1985 and 1995. Funds are ranked every
quarter by their risk adjusted return measured over a three month period using market timing,
stock selection and mixed strategy models. Following which, the risk adjusted return of
deciles of funds over the subsequent three month period are measured.
While abnormal returns were reflected in the top decile of funds suggesting
persistence in mutual fund performance, Bollen and Busse (2005) argued that abnormal
returns cease to exist when funds are being evaluated over a longer time horizon. This reflects
that superior performance is short lived and only significant when they are evaluated
regularly. Taking into consideration of account transaction cost and taxes, superior returns
may be generated by passive strategies like the buy and hold strategy compared to a
performance chasing strategies even if short term performance is foreseeable.
2.4.9 Business Cycles and Predictability Skills
Avramov and Wermers (2006) reported that most investment profits are generated
from the predictability in manager skills. Based on prior research, most studies find that
passive strategies have consistently outperformed active strategies. However, an article
focusing on stock picking skills reported that active management achieved significant returns
when examined during recession periods in comparison to expansion periods (Moskowitz,
2000). This suggests that business cycle variables may be advantageous in identifying
actively managed mutual funds that outperform.
Avramov and Wermers (2006) designed optimal portfolios of no load, open-end US
domestic equity mutual funds in the presence of manager selectivity and benchmark timing
skills, mutual fund risk loadings and benchmark returns. They analysed both ex post out of

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Quarterly Trades
sample performance and ex ante investment opportunity set delivered by predictability based
strategies. When an investor does not have any predictability skills, he or she will invest in
index funds. In comparison, investors that believed in the possibility of predicting fund risk
loadings and benchmark returns will invest in actively managed funds.
Avramov and Wermers (2006) analysed the optimal portfolio of 1301 open-end, no
load US domestic equity mutual funds which include index funds, sector funds, actively
managed funds and exchange traded funds. These mutual funds’ monthly database was over
the sample period between 1975 and 2002. Results reflected that incorporating predictability
skills makes actively managed funds more attractive and these funds generated larger Sharpe
ratios. Out of sample optimal portfolios that did not incorporate any predictability skills
produced negative alphas. By incorporating manager’s skills in predictability into long term
strategies, these strategies had outperformed their Fama-French and momentum benchmarks
by 2% to 4% per year by timing industries over business cycle and additional 3% to 6% per
year by choosing funds that outperform the industry benchmarks.
Avramov and Wermers (2006) found that predictability in manager skills are the
leading basis of investment profitability. Active management adds significant values in their
investment and industries are important in locating outperforming mutual fund. Also,
investment strategies that incorporated predictability manager selectivity and benchmark
timing skills consistently outperform. Predictability skill strategies performed best during
recessions but are also good during expansion. These skills are able to identify the best
performing funds during both expansion and recessions. Overall, active management of
mutual funds adds on significant values.
2.4.10 Stockholdings versus Trades

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Quarterly Trades
Numerous of studies have concluded that on average, mutual funds are not able to
outguess the market. Actively managed funds have underperformed their benchmark
portfolios. Although there has been no significant evidence of successful fund possessing
market timing or stock selection abilities to surpass the market, investors have continued to
invest in actively managed funds in hopes of achieving abnormal returns. Articles have
shifted their attention on evaluating the performance of stockholdings and trades of mutual
funds. Majority of these studies have focused on evaluating stock selection abilities with the
use of stockholdings and trades. Regardless, we examine how stockholdings and trades will
result in different values of active trading strategies.
2.4.10.1 Market Timing Abilities
Early studies have evaluated the returns of mutual funds but find no significant
evidence of market timing abilities. Jiang, Yao and Yu (2007) reported that these return-
based test are exposed to “artificial timing” biasness. They proposed an alternative market
timing measurement using mutual fund holdings to investigate the active changes of fund
betas as these holdings are not subjected to artificial timing biasness.
Using holdings, Jiang, Yao and Yu (2007) estimated the beta of a fund as the
weighted average of the individual stocks’ betas from the portfolio holdings and directly
tested if the covariance between the fund betas at the initial holding period and the market
returns of the holding period is significant. In comparison to return based measures that relied
on ex post realised returns to estimate the adjustments of beta, these measurements based on
holdings used only ex ante information. Therefore, these measures do not suffer from any
biasness by subsequent trading activities in the course of a holding period or dynamic trading
effect. In addition these holding based measures have better statistical power.

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Quarterly Trades
Jiang, Yao and Yu (2007) evaluated 2,294 actively managed equity mutual funds by
applying both return-based test and holdings-based test from the period between 1980 and
2002 using a single index model. Monthly observation data of returns of mutual funds are
used for the return based test and quarterly holdings of mutual funds are used for the holdings
based test. Jiang, Yao and Yu (2007) reflected that the results from the return based measures
exhibited similar results from majority of the research that on average, mutual funds have
slightly negative but insignificant market timing abilities. Whereas the results reflected from
holdings based measure suggested that on average, mutual funds have positive timing
abilities.
Implementing the alternative market timing measurement, Jiang, Yao and Yu (2007)
conducted the holding based test using active changes of fund beta and results suggested that
mutual funds time the market through active trading. While linking several fund
characteristics and market timing performances, they discovered market timing funds are
typically funds with high industry concentration, particularly those with a tilt towards small
cap stocks and with large fund size. Additionally, they stated that fund managers adjust fund
betas in accordance to macroeconomic variables such as price to earnings ratios and total
dividend yield. When macroeconomic variables are controlled, average market timing
abilities still appeared to be positive. This suggest that fund managers are not only utilising
information from the publicly accessible macroeconomic information but also private
information to time the market.
Following up on the study by Jiang, Yao and Yu (2007), Elton, Gruber and Blake
(2012) re-examined the existence of market timing abilities with the use of monthly portfolio
holdings instead of quarterly portfolio holdings. Elton, Gruber and Blake (2012) believed that
the use of monthly holdings data will capture a vast number of trades that are missed by
quarterly holdings data and provide a better estimation of timing trades. Unlike, Jiang, Yao

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Quarterly Trades
and Yu that only investigated the market timing abilities of actively traded equities, Elton,
Gruber and Blake (2012) investigated a full range of securities like options, futures, preferred
stock, bonds and non-traded equity. They reported that these range of securities use
additional instruments to time and ignoring their presence will result in inaccurate results of
market timing decisions.
In the study by Jiang, Yao and Yu (2007) which found positive market timing
abilities, they estimated portfolio betas with the use of portfolio holdings and security betas
and investigated the effects of changing betas with a single index model. Elton, Gruber and
Blake (2012) investigated if similar results will be exhibited when a multi index model is
used. The multi index model recognises bonds as an individual vehicle for timing.
Furthermore, they also re-examined market timing abilities with the used the Fama-French
model both with unconditional and conditional betas and a model that studies the effect of
adjusting allocation across industries.
Elton, Gruber and Blake (2012) examined the monthly data of holdings between the
periods from 1994 to 2005. Based on the results, negative timing abilities were reflected.
Results from the Fama-French model suggest that timing decisions of fund managers led to a
decrease in performance regardless being measured using conditional or unconditional
sensitivities. Similarly, the sector allocation’s model also reflected negative timing measures.
Inconsistent to the results from the single index model, the results from the two index
model reflected a different conclusion. Elton, Gruber and Blake (2012) discovered that the
timing decisions of mutual funds did not result in superior returns. First, when the managers
change their exposure to the market, they do so by adjusting their exposure to small stocks or
higher growth stocks. Taking into account of this shifting procedure, timing results was
altered as such unlike the single index model which reflected positive timing abilities, the two

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Quarterly Trades
index model exhibited negative timing abilities. Second, Elton, Gruber and Blake (2012)
reported that a large number of trades have been neglected with the use of quarterly holdings.
Third, the use of a wider range of securities may have impacted the results and that the major
contribution of negative timing abilities were from high technology stocks. Although no
market timing abilities were exhibited, Elton, Gruber and Blake (2012) showed that with
monthly holdings, timing ability can be measured more precisely as compared to using
quarterly or yearly data which misses a large number of trades.
While Jiang, Yao and Yu (2007) concluded that positive timing abilities are
significant, the study by Elton, Gruber and Blake (2012) questioned the credibility of their
results. Jiang, Yao and Yu (2007) assumes that the beta on the market of all securities that are
not traded equity is zero, as a result non-traded equity, bonds, futures, options, preferred
stocks and mutual funds are treated as identical instruments with each of them having a beta
on the market of zero. Elton, Gruber and Blake (2012) reported that 18.5% of trades by an
average fund manager were not captured when market timing measures were applied to
quarterly data holdings. In addition, even though Jiang, Yao and Yu (2007) found market
timing abilities using a single-index model, these findings did not hold up when a two-index
model was used.
2.4.10.2 Stock Selection Abilities
Chen, Jegadeesh and Wermers (2000) studied the value of active mutual fund
management by evaluating the stockholdings and trades of mutual funds. By examining both
stockholdings and trades, it resolves the issue of whether a stock truly signifies superior
information in regards to the stock’s value. They presumed that active stock trades represents
a stronger opinion of a manager in comparison to a passive decision of holding an existing
position in a stock as stockholdings may be prompted by reasons in relation to non-

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Quarterly Trades
performance like transaction costs and capital gain taxes. Therefore, they expect any evidence
of stock selection abilities to exhibit from the examination of trades compared to holdings.
Chen, Jegadeesh and Wermers (2000) compared the returns from stock holdings and
trades by high turnover funds and the returns from stock holdings and trades by low turnover
funds of 2,424 mutual funds quarterly data from the period between 1975 and 1995 to study
the value of active mutual fund management.
Based on the results from the examination of stock holdings, Chen, Jegadeesh and
Wermers (2000) found no difference in the performance of stocks that are most widely held
by mutual funds and those that are least widely held. However, when examining mutual fund
trades, stocks buys significantly gave higher returns than stocks sold. They concluded that
examining trades would be a better choice for portfolio performance examining trades as it is
a more powerful metric to determine the existence of superior information.
Pinnuck (2003) examined the performance of Australian fund managers’ monthly
stock holdings as well as trades of mutual funds to investigate if they possess superior
information. Stockholdings allow a more accurate examination of performance as compared
to traditional performance measures that relied on the examination of mutual funds’ return.
The examination of mutual funds’ trades is motivated by the study of Chen, Jegadeesh and
Wermers (2000) as the study showed that trades of mutual funds are more likely to represent
a signal of private information compared to passive stockholdings.
Pinnuck (2003) evaluated the stockholdings and trades of 35 Australian active equity
fund managers using their monthly portfolio holdings data from the period between 1990 and
1997. Unlike previous studies that examine the performance of stocks held at calendar quarter
ends, Pinnuck (2003) examined month end portfolios as they argued that quarter end
portfolios may not fully represent a typical fund portfolio and in addition have reporting

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Quarterly Trades
biases. Taking into consideration of the study by Chen, Jegadeesh and Wermers (2000) which
argued that studying trades of mutual funds would be a more powerful metric to determine
the existence of superior information. Pinnuck (2003) also investigated on the performance of
the stocks a fund manager trades, specifically stocks buys and stocks sold.
Based on the results, stocks held by fund managers on average, realised abnormal
returns. The results from the evaluation of individual trades showed that stocks that are
purchased by fund managers achieve abnormal returns but stocks sold did not exhibit any
abnormal returns. This suggests that fund managers do not possess superior information with
regards to bad news.
Pinnuck (2003) concluded that overall fund managers have the ability to select stocks
that realised positive abnormal returns. However, there were some limitations of this study.
Due to a limited time period, results may be time period specific. Also due to a small sample
size, results may be sample specific too. Trades exhibiting abnormal returns may result from
the consequences of price pressure rather than fundamental information. Survivorship
biasness which is the tendency for mutual funds with poor performance to be dropped by
mutual fund managers may have some impact on the resulted abnormal returns.
Baker, Litov, Wachter and Wurgler (2010) developed an alternative method of
identifying trading skills. They studied the nature of stock picking abilities and constructed
measures of trading skills built on how stocks are traded and held by fund managers perform
at subsequent corporate earnings announcement. It is the ability to buy stocks that are about
to enjoy high returns prior to their upcoming quarterly earnings announcements and sell
stocks prior to the suffering of low returns upon that announcement. This method enables a
more powerful approach to detect skilled trading and attempts to differentiate between the
winners from losers based on their trading activities. They believed that this approach will be

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