This document discusses incorporating news analysis into investment processes. It describes how news flows can be used to improve short-term risk assessments and condition risk estimates. Various data vendors that provide news analytics are also mentioned, as well as strategies for exploiting news signals, such as responding differently to "good" and "bad" news. Challenges with news-based strategies include defining events, assessing informational content, and managing holding periods.
Algorithmic strategy with adoptable trading frequency, effectively works with relatively inefficient markets. To the attention of potential investors/partners.
Algorithmic strategy with adoptable trading frequency, effectively works with relatively inefficient markets. To the attention of potential investors/partners.
Hedge Fund Predictability Under the Magnifying Glass:The Economic Value of Fo...Ryan Renicker CFA
The recent financial crisis has highlighted the need to search for suitable models forecasting hedge fund performance.
This paper develops and applies a framework in which to assess return predictability on a fund-by-fund basis.
Using a comprehensive sample of hedge funds during the 994-2008 period, we identify the fraction of funds in each style that are truly predictable, positively or negatively, by macro variables.
Out-of-sample, exploiting predictability can be di¢ cult as estimation risk and model uncertainty lead to imprecise fund forecast.
Moreover, in our multi-fund setting, investors face a trade-o¤ between unconditional and predictable performance, as strongly predictable funds may exhibit low unconditional mean.
Nevertheless, a strategy that combines forecasts across predictors circumvents all these challenges and delivers superior performance.
We highlight the statistical and economic drivers of this performance, especially in periods when predictor values strongly depart from their long run means.
Finally, we use one such period, the 2008 crisis, as a natural out-of-sample experiment to validate the robustness of our findings.
Volatility in Indian Stock Market: A study to assess volatility, persistence ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The much-heralded decoupling of the financial markets between developed, emerging and frontier markets met its nemesis in the 2008/9 global financial crisis- The Great Recession. For the believer in a diversified global basket of stocks or indices this came as a crushing blow. This notwithstanding, we still believe that a globally-diversified passive buy-and-hold strategy provides the best chance at maximising net investment return. We test this empirically.
Originally published in 2005. Abstract: Over the years many commodity trading advisors, proprietary traders, and global macro hedge funds have successfully applied various trend following methods to profitably trade in global futures markets. Very little research, however, has been published regarding trend following strategies applied to stocks. Is it reasonable to assume that trend following works on futures but not stocks? We decided to put a long only trend following strategy to the test by running it against a comprehensive database of U.S. stocks that have been adjusted for corporate actions. Delisted companies were included to account for survivorship bias. Realistic transaction cost estimates (slippage & commission) were applied. Liquidity filters were used to limit hypothetical trading to only stocks that would have been liquid enough to trade, at the time of the trade. Coverage included 24,000+ securities spanning 22 years. The empirical results strongly suggest that trend following on stocks does offer a positive mathematical expectancy, an essential building block of an effective investing or trading system.
Deutsche Bank Quantitative Strategies Research: The Wisdom Of Crowds, Crowdso...Leigh Drogen
Our initial findings show that the more timely Estimize forecasts provide greater short-term accuracy when compared to IBES. We find Estimize is more accurate than IBES for estimates taken one-week before the announcement date. We find that the timelier Estimize forecasts can more accurately identify earnings surprise which results in a greater capture of the post earnings drift. We use this finding to construct a daily trading strategy that goes long the stocks that beat the Estimize consensus and short the stocks that miss.
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F- S.docxLucasmHKChapmant
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F. Stambaugh NBER Working Paper No. 8462 September 2001 JEL No. G12 ABSTRACT This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34-year period, the average retum on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5% annually, adjusted for exposures to the market return as well as size, value, and momentum factors. 1. Introduction In standard asset pricing theory, expected stock returns are related cross-sectionally to returns' senxitivities to state variables with pervasive effects on consumption and invertment opportunities. The basic intuition is that a security whose lowest returns tend to accompany unfavorable shifts in quantities afferting an imvestor's overall welfare must offer additional compensation to the investor for holding that security. Liquidity appears to be a good candidate for a priced state variable. It is often viewed as important for investment decisions, and recent studies find that fluctuations in various measures of liquidity are correlated acroos stocks." This empirical study investigates whether market-wide liquidity is indeed priced. That is, we ask whether cross-sectional differences in expected stock returns are rehated to the sensitivities of returns to fluctuations in aggregate liquidity. 2 Liquidity is a broad and elusive concept that generally denotes the ability to trade large quantities quickly, at low cost, and without moving the price. We focus on an aspect of liquidity associated with temporary price fluctuations induced by order flow. Our monthly aggregate liquidity measure is a cross-sectional average of individual-stock liquidity measures. Each stock's liquidity in a given month, etimated using that stock's within-month daily returns and volume, represents the average effect that a given volume on day d has on the return for day d + 1 , when the volume is given the same sign as the return on day d . The basic idea is that, if signed volume is viewed ronghly as "order flow," then lower liquidity is reflected in a greater tendency for order flow in a given direction on day d to be followed by a price change in the opposite direction on day d + 1 . Esentially, lower liquidity corresponds to stronger volume-related return reversals, and in this respect our liquidity measure follows the same line of reasoning as the model and empirical evidence presented by Campbell, Groseman, and Wang (1993). They find that sturns accompanied by high volume tend to be reversed more strongly, and they explain how this result i.
Hedge Fund Predictability Under the Magnifying Glass:The Economic Value of Fo...Ryan Renicker CFA
The recent financial crisis has highlighted the need to search for suitable models forecasting hedge fund performance.
This paper develops and applies a framework in which to assess return predictability on a fund-by-fund basis.
Using a comprehensive sample of hedge funds during the 994-2008 period, we identify the fraction of funds in each style that are truly predictable, positively or negatively, by macro variables.
Out-of-sample, exploiting predictability can be di¢ cult as estimation risk and model uncertainty lead to imprecise fund forecast.
Moreover, in our multi-fund setting, investors face a trade-o¤ between unconditional and predictable performance, as strongly predictable funds may exhibit low unconditional mean.
Nevertheless, a strategy that combines forecasts across predictors circumvents all these challenges and delivers superior performance.
We highlight the statistical and economic drivers of this performance, especially in periods when predictor values strongly depart from their long run means.
Finally, we use one such period, the 2008 crisis, as a natural out-of-sample experiment to validate the robustness of our findings.
Volatility in Indian Stock Market: A study to assess volatility, persistence ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The much-heralded decoupling of the financial markets between developed, emerging and frontier markets met its nemesis in the 2008/9 global financial crisis- The Great Recession. For the believer in a diversified global basket of stocks or indices this came as a crushing blow. This notwithstanding, we still believe that a globally-diversified passive buy-and-hold strategy provides the best chance at maximising net investment return. We test this empirically.
Originally published in 2005. Abstract: Over the years many commodity trading advisors, proprietary traders, and global macro hedge funds have successfully applied various trend following methods to profitably trade in global futures markets. Very little research, however, has been published regarding trend following strategies applied to stocks. Is it reasonable to assume that trend following works on futures but not stocks? We decided to put a long only trend following strategy to the test by running it against a comprehensive database of U.S. stocks that have been adjusted for corporate actions. Delisted companies were included to account for survivorship bias. Realistic transaction cost estimates (slippage & commission) were applied. Liquidity filters were used to limit hypothetical trading to only stocks that would have been liquid enough to trade, at the time of the trade. Coverage included 24,000+ securities spanning 22 years. The empirical results strongly suggest that trend following on stocks does offer a positive mathematical expectancy, an essential building block of an effective investing or trading system.
Deutsche Bank Quantitative Strategies Research: The Wisdom Of Crowds, Crowdso...Leigh Drogen
Our initial findings show that the more timely Estimize forecasts provide greater short-term accuracy when compared to IBES. We find Estimize is more accurate than IBES for estimates taken one-week before the announcement date. We find that the timelier Estimize forecasts can more accurately identify earnings surprise which results in a greater capture of the post earnings drift. We use this finding to construct a daily trading strategy that goes long the stocks that beat the Estimize consensus and short the stocks that miss.
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F- S.docxLucasmHKChapmant
Liquidity Risk and Expected Stock Returns Lubos Pastor and Robert F. Stambaugh NBER Working Paper No. 8462 September 2001 JEL No. G12 ABSTRACT This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34-year period, the average retum on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5% annually, adjusted for exposures to the market return as well as size, value, and momentum factors. 1. Introduction In standard asset pricing theory, expected stock returns are related cross-sectionally to returns' senxitivities to state variables with pervasive effects on consumption and invertment opportunities. The basic intuition is that a security whose lowest returns tend to accompany unfavorable shifts in quantities afferting an imvestor's overall welfare must offer additional compensation to the investor for holding that security. Liquidity appears to be a good candidate for a priced state variable. It is often viewed as important for investment decisions, and recent studies find that fluctuations in various measures of liquidity are correlated acroos stocks." This empirical study investigates whether market-wide liquidity is indeed priced. That is, we ask whether cross-sectional differences in expected stock returns are rehated to the sensitivities of returns to fluctuations in aggregate liquidity. 2 Liquidity is a broad and elusive concept that generally denotes the ability to trade large quantities quickly, at low cost, and without moving the price. We focus on an aspect of liquidity associated with temporary price fluctuations induced by order flow. Our monthly aggregate liquidity measure is a cross-sectional average of individual-stock liquidity measures. Each stock's liquidity in a given month, etimated using that stock's within-month daily returns and volume, represents the average effect that a given volume on day d has on the return for day d + 1 , when the volume is given the same sign as the return on day d . The basic idea is that, if signed volume is viewed ronghly as "order flow," then lower liquidity is reflected in a greater tendency for order flow in a given direction on day d to be followed by a price change in the opposite direction on day d + 1 . Esentially, lower liquidity corresponds to stronger volume-related return reversals, and in this respect our liquidity measure follows the same line of reasoning as the model and empirical evidence presented by Campbell, Groseman, and Wang (1993). They find that sturns accompanied by high volume tend to be reversed more strongly, and they explain how this result i.
Since the CAPM model Sharpe (1965) and the first “fundamental” model by King (1966) the use of “factors” in alpha generation and risk modeling has become mainstream. However, the types of factors we employ and the techniques we use to model relationships have in general not progressed much since. In addition, many of our favorite techniques assume that the world is static, whereas of course markets evolve and change dramatically; as we have seen so vividly illustrated over the last few years.
We review fundamental, macro-economic, and statistical factors, describing the advantages and disadvantages of each, and review some newer techniques that explicitly allow for evolving relationships in data sets and harness emerging technologies that can capture much more nuanced relationships than simple correlation: “flexible” least-squares regression, artificial immune systems, single-pass clustering, semantic clustering, social network influence measurement, layer-embedded networks, block-modeling, and more.
MODELING THE EXTREME EVENTS OF THE TOP INDUSTRIAL RETURNS LISTED IN BSEIAEME Publication
Extreme price movements in the financial markets are rare, but important the objective of study was to evaluate the extreme events of major industries in BSE. The study was conducted for returns of industries and shows the extreme events to which the industries are scattered for their returns. Many models were undertaken as base for the study, to identify the extreme events of the industries and same has been incorporated for the analysis too.
The paper opens with an overview of the
commodity trading advisor (CTA) sector, highlighting the
significant growth that has taken place in the managed
futures industry in recent years and explaining how
the managed futures strategies that CTAs employ
work in practice. The breadth of sub-strategies under
the managed futures umbrella are then examined.
The third part of the paper examines the benefits and
perceived risks to investors of allocating to managed
futures strategies and also addresses various common
misunderstandings about CTAs.
The paper concludes by exploring the common ways
as to how investors can access the various investment
strategies that are available
Using Cross Asset Information To Improve Portfolio Risk Estimationyamanote
There are obvious relationships between the various securities of a given firm that impact our expectations of risk. For example, if fixed income investors expect a corporate bond of a company to default, there must be a related bankruptcy event that would negatively impact shareholders in that firm. In this presentation, Nick will describe how to use data from bond and option markets to improve risk estimation for equity portfolios, and how to use information from the equity markets to improve estimation of credit risk in fixed income securities. The goal of the process is to create holistic risk estimation where all expectations of risk are mutually consistent across the entire capital structure of a firm, and related derivatives.
New Report 1SummaryFrom the USA Today article, Sprea.docxcurwenmichaela
New Report 1
Summary:
From the USA Today article, “Spread the Wealth: It’s Not Just ‘Popular’ Stocks that Go Up”, Adam
Shell illustrates that current investors are largely “attached” to Wall Street’s high-profile company
stocks; companies such as Tesla, Apple, and Facebook from the technology sector receive the
most buy orders from investors because these companies “get the most press coverages, the most
PR, and the most adulation.” The author emphasized that although these technology giants have
the most spotlight on the stock market, investors should not overlook less-glamorous corporate
stocks from other sectors because often times, the stocks from these “less-notable” companies go
under the radar and quietly rise in value.
This year so far, is a perfect example, according to Adam Shell, when auto parts makers were up
more than 25 percent, the gaming industry and casinos have returns of more than 50 percent, and
homebuilding stocks jumped 30 percent. In addition, the hotel and resort industry has booked
gains 25 percent, stocks of health care equipments increased by 25 percent, and airlines have also
experienced growth, with the help of lower fuel costs. Shell suggested that these gains from
different sectors are viewed as healthy, as it implies that the investors are now investing in
companies that are not largely in the technology sector, which has been the market leader so far in
2017.
Relation to Text:
The article that I had chosen above relates to several concepts discussed in Chapter 8 from Bodie,
Kane, and Marcus’ Finance - Essentials of Investment. Chapter 8: Efficient Market Hypothesis,
describes that stock prices should follow the notion of random walk, that is, changes in stock prices
(fluctuations) should be random and unpredictable. Intelligent investors should equipment
themselves with relevant and up-to-date information on which to buy or sell stocks before the rest
of the market becomes aware of that same set of information. This applies to investors who are
vigorously investing largely in technology companies in the technology sector; they should also
diversify their investments into other sectors (based on relevant and helpful information) to reduce
risks. Technical analysis and the momentum effect were likely the dominant cause of investment
concentrations from investors. Technical analysis, the search on recurrent and predictable patterns
in stock prices and on proxies for buy and sell pressure in the market, allows investors to
strengthen their investment positions on a certain company in a certain stock market sector; the
investment trend is further bolstered due to the momentum effect, which states that the tendency of
poorly performing and well-performing stocks in one period to continue that abnormal performance
in the following periods.
Active and passive management and risk-return trade-off are part of the topics described in
chapter 1 from the text and these concepts also rela ...
New Report 1SummaryFrom the USA Today article, Sprea.docx
Forum On News Analytics
1. Forum on News Analytics applied to Trading, Fund Management and Risk Control
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5. Incorporation of Quantified News into Portfolio Risk Assessment Dan diBartolomeo Northfield and Brunel University London, 2009
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27. INCOPRORATING NEWS ANALYSIS INTO TRADING AND INVESTMENT PROCESSES FORUM ON NEWS ANALYTICS November 9, 2009 James Chenery Business Development Manager
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32. HOW TO MAKE MONEY WITH THE NEWSSCOPE ANALYTICS Buy on good news Sell on bad news Outperform S&P500 by 5000 basis points over a 60 day period! S&P1500 stocks in 2008; Daily items >50; Pos vs Neg >50%
37. Exploiting news-flow signals Macquarie Quantitative Research Gurvinder Brar, Christian Davies, Adam Strudwick, Andy Moniz [email_address] Macquarie Capital (Europe) Ltd Level 2, Moor House, 120 London Wall , London EC2Y 5ET November 2009
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43. Page Important disclosures: Recommendation definitions – For quarter ending 30 September 2009 Recommendation definitions Macquarie - Australia/New Zealand Outperform – return > 5% in excess of benchmark return Neutral – return within 5% of benchmark return Underperform – return > 5% below benchmark return Macquarie – Asia/Europe Outperform – expected return >+10% Neutral – expected return from -10% to +10% Underperform – expected <-10% Macquarie First South - South Africa Outperform – return > 10% in excess of benchmark return Neutral – return within 10% of benchmark return Underperform – return > 10% below benchmark return Macquarie - Canada Outperform – return > 5% in excess of benchmark return Neutral – return within 5% of benchmark return Underperform – return > 5% below benchmark return Macquarie - USA Outperform – return > 5% in excess of benchmark return Neutral – return within 5% of benchmark return Underperform – return > 5% below benchmark return Recommendation – 12 months Note: Quant recommendations may differ from Fundamental Analyst recommendations Volatility index definition* This is calculated from the volatility of historic price movements. Very high–highest risk – Stock should be expected to move up or down 60-100% in a year – investors should be aware this stock is highly speculative. High – stock should be expected to move up or down at least 40-60% in a year – investors should be aware this stock could be speculative. Medium – stock should be expected to move up or down at least 30-40% in a year. Low–medium – stock should be expected to move up or down at least 25-30% in a year. Low – stock should be expected to move up or down at least 15-25% in a year. * Applicable to Australian/NZ stocks only Financial definitions All "Adjusted" data items have had the following adjustments made: Added back : goodwill amortisation, provision for catastrophe reserves, IFRS derivatives & hedging, IFRS impairments & IFRS interest expense Excluded: non recurring items, asset revals, property revals, appraisal value uplift, preference dividends & minority interests EPS = adjusted net profit /efpowa* ROA = adjusted ebit / average total assets ROA Banks/Insurance = adjusted net profit /average total assets ROE = adjusted net profit / average shareholders funds Gross cashflow = adjusted net profit + depreciation *equivalent fully paid ordinary weighted average number of shares All Reported numbers for Australian/NZ listed stocks are modelled under IFRS (International Financial Reporting Standards). AU/NZ Asia RSA USA CA EUR Outperform 45.08% 54.02% 40.00% 42.31% 62.86% 43.61% Neutral 39.77% 19.10% 45.00% 43.36% 31.90% 39.85% Underperform 15.15% 26.88% 15.00% 14.34% 5.24% 16.54%
News is emerging as an exploitable content set to drive alpha and manage event risk. We’ve been in market for about 2 years and while most of the focus is more on black-box trading, regardless of trading frequency, we see many ways to utilize it for advanced human decision support especially with our news analysis solutions. Customers are utilizing machine readable news in several key areas from the obvious speed advantage to managing the scale and scope of their portfolio to creative risk management and loss avoidance strategies. NewsScope represents a robust set of capabilities and data for customers to utilize – from historical data for deep research, algo building and back testing to real-time ultra-low latency feeds for optimal execution, to news analytics where we convert qualitative text into quantitative values making it easier for machines and humans-alike to analyze and comprehend.
A number of academic and practitioner research has focused on news and the subsequent market impact. Some of the main conclusions fall into the following areas: News flow is a good indicator of volume and volatility. The more news on a company, the higher the volume traded & the more volatile it is (more about abnormal volume spikes – see Event Indices) Enhanced by relevance, sentiment, and novelty indicators Pricing movements accompanied by news tend to be momentum in nature; those with a lack of news tend to reverse to average trends. Applications for statistical arbitrage, momentum trading, technical analysis, risk, human decision support The market tends to overreact when there is a lot of news on something and under-react when there is a small quantity of news. Reversals as news is “repeated” and syndicated. Longer lead times / position building for more novel stories. For direction and magnitude, find cause:effect relationships Key words – upgrade/downgrade, raises/lowers guidance, misses/exceeds Market sentiment – results vs consensus Author sentiment – tone of news articles
Wolf detection / circuit breaker – automatically stop the algorithm when news is published – tends to be more defensive in nature News flow algorithms – because news flow can predict potential volume or volatility spikes, one might switch from VWAP to News VWAP, a more participatory algorithm, which accelerates an order to get a better price in the market. Alpha generating signal – being able to predict returns is key use to many Post Trade Analysis – assist in proving best execution and trader performance Stock screening tool – give me all of the good news and bad news stocks Risk Management – help understand event risk and what kinds of news can impact your portfolio risk profile Monitor for Compliance and market abuse – track insider trading Fundamental research – assist in calculating stock, sector, and market forecasts Trader decision support for confirming or contrarian trading signals
Note hosted and deployed solutions, fault tolerant, fully resilient for the best mix of flexibility and performance (as a differentiator vs Ravenpack, of course) and we also provide the market data with common symbology and timestamp making it easier to test and then deploy into production.
Note that given the limited amount of time, you can share some more detail and examples after the session, but I like to call this slide: “How to make money with the NewsScope Analytics” Buy on the good news (green); sell on the bad news (red) and outperform the S&P 500 by 5000 basis points in 60 days! (if only it were that easy)