1. AFE6014-B Empirical Methods In Accounting And Finance
Answer:
Different investors have different tolerance to risk. Other than age, goals and preferences,
risk return tradeoff also affects investors’ decision on risky investments. According to John
and Luis (2005), risk-return tradeoff explains that level of return from an investment
should increase with increase in risk levels. Therefore, a risk aggressive investor in a higher
risk investment does so in anticipation of greater probability of high returns and one who is
risk averse opts for lower risks investment and would expect lower returns.
Efficient Market Hypothesis suggests that investors in the market are rational and price of
assets can reflect fully their fair value (Fama 1970). Since the evolution of this theory,
financial theory has developed drastically. More and more phenomena on the financial
markets have prompted financial scientists to carry out more study on traditional finance.
Ricciardi and Simon (2000) identify behavioral finance as the cognitive factors and
emotional issues that affect the decision making process of individuals. This brings about
the element of investor sentiment that drives the attitude of investor towards a particular
stock.
Several studies have shown interest in investor behavior and the impact on investment
decisions. Yu and Yuan (2011) investigates the influence investor sentiment have on the
market mean-variance tradeoff. According to this study, stock market expected return is
positively related to market’s variance in low-sentiment periods. However, in high
sentiment period, the expected return has no relationship with variance. These investors
during high sentiment period undermine a positive mean variance tradeoff. Negative
correlation between returns and volatility innovation is therefore much stronger in low-
sentiment periods.
Wang (2018a) further probes into the impact of investor sentiment on the mean variance
relationship in fourteen European stock markets. The study tends to look into the fact that
miss estimating risk can distort the mean variance relationship. From the study, it is
established that high sentiment period undermines risk return tradeoff. This is due to
unwillingness of investor to take a short position therefore exerting huge impact on the
stock market. It is also noted that investor sentiment on mean variance relationship is not
2. supported in all markets but specific ones. For this reason, investor optimism is more
determined by normal sentiment state. Investor decision on stock trading depends on
nominal sentiment level disclosed by sentiment surveys as well as sentiment levels relative
to the normal sentiment state.
Another study is carried out on the role of institutional investor sentiment in the mean
variance relation Wang 2018b. the empirical results of the study found some contrasting
relatoionship with Yu and Yuan (2018). Here, the market returns were found to be
negatively correlted to the market conditional volatility in bullish market. The opposite was
found in a bearish market, where there was a positive relationship.
Traditional Finance Theory
Wang and Duxbury (2021) behavior of investor thought to be sophisticated and rational.
The study sought to identify the impact of investor sentiment on the mean-variance
relationship the CCI index was used for investor sentiment and 50 global stock markets was
used. The results showed that there is a negative relationship between investor sentiment
and the future returns on a global level. The study also, wanted to understand impact of the
investor sentiment on developed and developing nations. However, the negative patterns
are not disrupted in the developed and developing nations. They discovered that there is an
instant impact on the developing nations but there is a more enduring effect on the already
developed nations. They also, evaluated the impact on the individual stock markets and
there was heterogeneity in the returns. They attributed this to the differences in cultures
among the various markets.
Evidently, there is a difference in the relationship between investor sentiment and the
mean-variance relationship.v
Investor Sentiment Index
Sentiment can be measured either directly or indirectly, depending on the circumstances.
Information gathered through survey methods, such as asking participants about their
views on the stock market and economy, as well as through electronic means like the
internet and social media, is used to derive direct measures. Investor sentiment can be
gauged indirectly through financial and economic variables.
Both direct and indirect measures have been extensively used in the empirical literature.
Public opinion surveys were used by the likes of Lemmon and Portniagagina. A sentiment
index based on daily internet search volume collected from millions of households was
developed by Da et al. (2015) and called the FEARS index. Social media feeds were used to
gauge investor sentiment by Bollen et al. (2011) and the other researchers studied the
results of various World Cup games via websites. According to recent research, data derived
from internet searches, specifically Google trends, can be used to gauge uncertainty rather
3. than sentiment. Even the emotion of fear can be considered a feeling. Chicago Board of
Options Exchange's volatility index (VIX), based on 30-day volatility and market
expectations, was interpreted by Ghosh et al. (2007) as a measure of fear rather than
sentiment or uncertainty.
In spite of the fact that there isn't a single indicator for sentiment, it can provide
information about specific firm characteristics such as performance, liquidity, and activity
level. A sentiment index can therefore be built from a variety of individual indicators, as the
literature assumes. In this paper, we use the consumer sentiment index as provided by
Wang (2021) and the methodology of this paper still follows the procedure that is provided
by Wang (2021).
The focus of this paper is an emerging market and we study india. Frontier markets, also
known as "emerging markets," are developing economies that have less developed financial
and regulatory frameworks than mature markets. Despite the fact that they have low per
capita incomes, these economies are growing at a faster rate. For investors, India is the most
prominent emerging market to consider because of its strong manufacturing growth,
business-friendly reforms, infrastructure development, and political stability, among other
factors. According to the International Monetary Fund's World Economic Outlook, India's
economy will grow at a rate of 7.2 percent in fiscal year 2017 and 7.7 percent in fiscal year
2018, outpacing China's growth rate. In January 2017, an investment of $3.3 billion was
made in India's economy, confirming the country's economic stability in the emerging
markets.
Methodology
This section presents the methodology that was used to achieve the objectives of this paper.
Therefore, it’s divided into the data section, and the models that were used in this analysis.
Data
The data collected in this analysis was the investor sentiment and the stock returns.
According to Baker et al. (2012)., market-related indicators were used in conjunction with
theoretical and empirical models to develop an investor sentiment index that is consistent
over time In this study, the first principal component and lagged components were used to
generate four indirect proxies using the first principal component and lagged components.
The first significant component of the sample's variation can be accounted for by the second
significant component. The stock market variable that is selected for this analysis is the
Nifty 50 index. It is a weighted average of the 50 largest publicly traded companies in India,
as measured by the NIFTY 50 index. The BSE SENSEX is the other major stock market index
in India, and it is based in Mumbai.
Creation Of The Investor Sentiment
4. Investor sentiment is reflected more slowly in some proxies than in others, according to
industry experts. Baker and Wurgler (2006) used PCA (levels and lags) analysis to isolate
the most significant components of investor sentiment after collecting accurate data on the
subject.
Market liquidity can be gauged by looking at the share turnover ratio, which shows how
active the stock market is in terms of traders and investors, both of whom are good
indicators of market liquidity. Liquidity is determined by dividing the total number of
shares traded over a given time period by the average number of shares in circulation
during that time period, and the formula above can be used to do so The more frequently a
company's stock is traded, the greater its liquidity. For an ETF or mutual fund, the turnover
rate is different from that of a stock since it measures how frequently its assets are
exchanged. The share turnover ratio is used in the stock market to determine how easy or
difficult it is to sell shares of a particular stock on the open market. During a given time
period, it compares the total number of shares that could have been traded to the actual
number of shares that traded. To avoid a company with low share turnover, investors who
do not want to risk their money may avoid it. A market capitalization ratio can be calculated
by dividing the traded share count by the total value of traded shares. Increasing turnover
has a significant impact on market participants' perceptions. Irrational investors make the
market more profitable to trade when they are optimistic (Baker and Stein 2004). Baker
and Stein are partners in crime (Baker and Stein, 2004). Turnover should be inversely
proportional to return on investment (ROI) (Jones 2001).
The total number of shares traded on the stock exchange MT or the Market Turnover is
divided by the total amount of stock exchange trading to arrive at market turnover (MT). If
the volume of trading activity in an individual stock or the overall market is high, the
turnover rate will be high as well. The amount of rupees exchanged and the total number of
transactions are two ways to gauge transactional turnover. As more people want to buy the
stock, its liquidity has improved, making it easier to sell. By dividing the rupee turnover by
the market capitalization, we can arrive at the overall market turnover ratio. The level of
liquidity and the mood of the market are also important considerations. To determine
whether the market is bullish or bearish, the lower the turnover, the better (Karpoff 1987).
In 1987, Karpoff. Product pricing rises when turnover decreases, as shown by the data
presented above (Ying 1966).
As a measure of market breadth, the ADR (advanced and declining shares) accounts for
changes in the share price volatility over time. As the market changes, so does the ADR's
value (Brown and Cliff 2004). Brown and Cliff are two of the best friends in the world
(Brown and Cliff, 2004). It is expected that the ADR ratio will have a positive impact on the
stock market in the near future because of investor optimism. It is therefore possible to use
the ADR ratio to detect current trends and evaluate the health of the marketplace. It is
possible to get a sense of how the market has been doing over time by using the ADR to
5. compare it to an index like the New York Stock Exchange. Investors may be able to learn
more about what drives a market rally or selloff by comparing these two indicators. When a
market is oversold, the advance-decline ratio is lower than when a market is overbought.
Using this information to predict a market's reversal can be done using the advance-decline
ratio.
The put-call ratio (PCR) is another factor to keep in mind (PCR). Total Chinese put and call
option trades divided by total Nifty call option trades produces the following mood
indicator. The market is more confident when the positive (negative) relationship is
stronger (bearishness). The PCR method can be used to calculate the aggregate sentiment
index because it reflects market participants' expectations. If a downward trend is
predicted, investors will go to great lengths to safeguard their capital. There are more put
option trades than call option trades in any given period of time, and this is indicated by a
higher put option trade volume to call option trade volume ratio (Brown and Cliff 2004;
Finter and Ruenzi 2012). There are two recent examples: Finter and Ruenzi (2012; Brown
and Cliff, 2004). Due to its low negative market PCR, this derivative market proxy is seen as
a precursor to a bullish market movement in the near future (Brown and Cliff 2004). Brown
and Cliff are two of the best friends in the world (Brown and Cliff, 2004).
For each of the variables in the data, there are three sets that are collected for the year
2020. The daily prices are collected and stored in a Microsoft excel file where they are
analyzed using R statistical programming software.
Principal Component Analysis
It is indicated by the i-th vector in a collection of points in real coordinate space which
direction the line that best fits the data while remaining orthogonal to the first i-1 vectors
should be drawn to best fit the data. According to this rule, the most optimal line in this
situation is one that reduces the average squared distance between each point and the line
to the smallest value possible. When the individual dimensions of the data are not linearly
correlated, an orthonormal basis is used to describe the data. This method, known as
principal component analysis, allows you to alter the data's foundation by utilizing only a
few of the principal components (PCA).
The use of PCA can be beneficial for both data exploration and the development of
predictive models. When dimensionality reduction is desired, it is common practice to
project each data point onto the first few principal components of the data set in order to
produce lower-dimensional data with the greatest amount of variability. The first principal
component is chosen in such a way that the variance of the projected data is maximized as
much as possible. The principal component analysis as used in this paper is to get the
investor sentiment. There are a ttotal of four variables that are used to get the index. These
are.
6. The GARCH Model
It was named after Nobel Prize-winning economist Robert F. Engle, whose contributions to
the field were recognized in 2003 with the establishment of the Garch procedure. The
GARCH method for predicting the volatility of the financial markets is introduced in this
research. There are numerous approaches that can be used to implement GARCH modeling.
A more realistic backdrop than other models is what motivates financial analysts to use the
GARCH process to forecast prices and interest rates for financial instruments, rather than
using other models altogether (Hu, et al., 2020).
The method developed by Dr. Tim Bollerslev while working on his PhD in 1986 was a
method for predicting asset value volatility. Dr. Bollerslev was the one who came up with
the term GARCH. Robert Engle's seminal work on the Autoregressive Conditional
Heteroskedasticity (ARCH) model, which was published in 1982, was considered a
watershed moment in economic history. As he built his model, he anticipated that the
variance of financial returns would be conditional and dependent on one another rather
than stable as he predicted. In the recent bout of stock market volatility, we have seen an
excellent illustration of this principle in action (Liu and So, 2020).
GARCH has undergone a number of transformations over the course of the project's
existence. The application of nonlinear and integrated Garhi (NGARCH and IGARCH) models
can be used to resolve an apparent "volatility clustering" problem. When using any of the
GARCH models, remember to take into account both positive and negative returns, as well
as the magnitude of returns (as handled in the original model) (addressed in the original
model). It is possible to modify the specific characteristics of a stock, industry, or economic
data by using any of the GARCH derivations. Financial institutions calculate the Value-at-
Risk (VAR) for a specific time period using GARCH models, which are derived from the
generalized estimating equation (whether for a single investment or trading position, a
portfolio, or at a division or firm-wide level). It is believed that Garch models, rather than
standard deviations, are more accurate predictors of future risks.
Results
The results of the model are shown in the graph below. For both the ARCH and GARCH
coefficients, this represents a statistically significant departure from zero. When these two
numbers are added together, the sum of these two numbers is also close to one. In response
to the constant shocks, the mean reversal process is extremely slow to begin with. Given
that the ARCH-LM test revealed no additional ARCH effects, the model accurately depicts
ARCH effects, as demonstrated by the results of the test. After 20 lags in the model, the Q
and Q2 coefficients are no longer statistically significant, indicating that autocorrelation has
been eliminated.
The Garch model generates tables that can be used to interpret the model's coefficients and
7. operation in R. These tables can be found here.
Looking at the first estimation table, it becomes clear that the least likely values are the ones
most likely to produce the best results. This was accomplished through the determination of
the significance of the estimated parameter, as shown in the following table. It is
demonstrated that Omega1 (parameter w1) is ineffective in this model when it is used as a
constant parameter. The second table below contains a list of criteria that can be used to
help you evaluate different types of information. Models such as Akaike and Bayes, Hannan-
Quinn and Shibata types of equation models are illustrated as well as their components,
such as their equations and their coefficients (AIC, BIC, and Shibata, respectively) When
these parameters are kept to a minimum, the model's fit is improved.
Volatility Curve
In the graph above, we can see how volatility has changed over the years. From the
beginning to the end of the year, volatility rose significantly.
Conclusion
An investor's mood has a direct impact on the stock market's behavior. Capital asset pricing
model theory holds that investors should be rewarded for taking on risk in order to
increase their returns on investment. Investors can expect lower returns and greater
market volatility when inflationary expectations rise. If they are not compensated for the
expected volatility with a market risk premium, market participants will sell their positions.
In this vicious cycle, a downturn in the stock market or sluggish Economic Growth would be
undesirable outcomes. In terms of the conditional volatility graph, negative sentiment has a
greater impact. As a result, when investors are feeling upbeat, the market is more likely to
see them participate. As a result, there may be an increase in market speculation, which
could lead to an overvaluation of stock prices. Investors who are concerned about future
returns tend to pull their money out of the market when it is in a bear market. The number
of initial public offerings (IPOs) may increase as a result of the increased optimism (IPOs).
The stock price of a company rises immediately after announcements of dividends, rights
offerings, and other corporate actions.
References
Baker, M. and Stein, J.C., 2004. Market liquidity as a sentiment indicator. Journal of financial
Markets, 7(3), pp.271-299.
Baker, W.E., Cox, P.A., Kulesz, J.J., Strehlow, R.A. and Westine, P.S., 2012. Explosion hazards
and evaluation. Elsevier.
Bollen, J., Mao, H. and Zeng, X., 2011. Twitter mood predicts the stock market. Journal of
8. computational science, 2(1), pp.1-8.
Brown, G.W. and Cliff, M.T., 2004. Investor sentiment and the near-term stock market.
Journal of empirical finance, 11(1), pp.1-27.
Fama, E.F., 1970. Session topic: stock market price behavior. The Journal of Finance, 25(2),
pp.383-417.
Finter, P., Niessen-Ruenzi, A. and Ruenzi, S., 2012. The impact of investor sentiment on the
German stock market. Zeitschrift für Betriebswirtschaft, 82(2), pp.133-163.
Ghosh, M., 2007. Fundamentals of experimental pharmacology. Indian Journal of
Pharmacology, 39(4), pp.216-216.
Jones, E.P., 2001. Circulation in the arctic ocean. Polar Research, 20(2), pp.139-146.
Karpoff, J.M., 1987. The relation between price changes and trading volume: A survey.
Journal of Financial and quantitative Analysis, 22(1), pp.109-126.
Ricciardi, V. and Simon, H.K., 2000. What is behavioral finance?. Business, Education &
Technology Journal, 2(2), pp.1-9.
Wang, W., 2018. Investor sentiment and the mean-variance relationship: European
evidence. Research in International Business and Finance, 46, pp.227-239.
Wang, W., Su, C. and Duxbury, D., 2021. Investor sentiment and stock returns: Global
evidence. Journal of Empirical Finance, 63, pp.365-391.
Ying, C.C., 1966. Stock market prices and volumes of sales. Econometrica: Journal of the
Econometric Society, pp.676-685.
Yu, J. and Yuan, Y., 2011. Investor sentiment and the mean–variance relation. Journal of
Financial Economics, 100(2), pp.367-381.
