Market Predictability - page 2

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Return Predictability in International Financial Markets and the Role of Investor Sentiment : return_predictability_in_international_financial_markets_and_the_role_of_investor_sentiment.pdf

We investigate the predictability of stock returns in the financial market for a large panel of developed countries using investor sentiment, business-cycle variables and financial indicators within two panel regime-switching models, with threshold and smooth transition between regimes. We nd strong evidence of predictability of long-term returns following the business cycles, but much weaker results for the short-run returns. During crisis times, investor sentiment and inflation become key factors in predicting stock returns. Differenttests and goodness of t measures point out that the use of regime-switching models is more appropriate than linear models. To our knowledge, this study is the first to examine the impact of investor sentiment on future returns for a large number of countries, the existing literature being mainly focused on the U.S. stock market

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Hurst exponent and prediction based on weak-form efficient market hypothesis of stock markets : hurst_exponent_and_prediction_based_on_weak-form_efficient_market_hypothesis_of_stock_markets.pdf

We empirically investigated the relationships between the degree of efficiency and the predictability in financial time-series data. The Hurst exponent was used as the measurement of the degree of efficiency, and the hit rate calculated from the nearest-neighbor prediction method was used for the prediction of the directions of future price changes. We used 60 market indexes of various countries. We empirically discovered that the relationship between the degree of efficiency (the Hurst exponent) and the predictability (the hit rate) is strongly positive. That is, a market index with a higher Hurst exponent tends to have a higher hit rate. These results suggested that the Hurst exponent is useful for predicting future price changes. Furthermore, we also discovered that the Hurst exponent and the hit rate are useful as standards that can distinguish emerging capital markets from mature capital markets.

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Evidence of predictability in the cross-section of bank stock returns : evidence_of_predictability_in_the_cross-section_of_bank_stock_returns.pdf

In this paper, we examine the predictability of the cross-section of bank stock returns by taking advantage of the unique set of industry characteristics that prevail in the financial services sector. We examine predictability in the cross-section of bank stock returns using information contained in individual bank fundamental variables such as income from derivative usage, previous loan commitments, loan-loss reserves, earnings, and leverage. We find that variables related to non-interest income, loan-loss reserves, earnings, leverage, and standby letters of credit are all univariately important in forecasting the cross-section of bank stock returns. Surprisingly, neither book-to-market nor firm size is important in our sample. We examine whether this cross-sectional predictability is due to increased risk, or another explanation, such as investor under or overreaction. Our results suggest that this predictability is not due to increased risk, but rather is consistent with investor underreaction to changes in banks
fundamental variables. Furthermore, out-of-sample testing demonstrates this underreaction appears to be exploitable using simple cross-sectional trading strategies.

[sebatian]

Thanks for the books

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The Efficient Market Hypothesis and Its Critics : the_efficient_market_hypothesis_and_its_critics.pdf

A generation ago, the efficient market hypothesis was widely accepted by academic financial economists; for example, see Eugene Fama’s (1970) influential survey article, “Efficient Capital Markets.” It was generally believed that securities markets were extremely efficient in reflecting information about individual stocks and about the stock market as a whole. The accepted view was that when information arises, the news spreads very quickly and is incorporated into the prices of securities without delay. Thus, neither technical analysis, which is the study of past stock prices in an attempt to predict future prices, nor even fundamental analysis, which is the analysis of financial information such as company earnings, asset values, etc., to help investors select “undervalued” stocks, would enable an investor to achieve returns greater than those that could be obtained by holding a randomly selected portfolio of individual stocks with comparable risk.

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Is Time-Series-Based Predictability Evident in Real Time? : is_time-series-based_predictability_evident_in_real_time.pdf

There now appears to be overwhelming evidence of stock market predictability. A large body of research shows that excess returns on the aggregate market are forecastable from the default spread, dividend yield, dividend payout, the term spread, consumption data, inflation, industrial production, wealth, and labor income, to name but a few variables.1 Yet, despite this seemingly overwhelming evidence, there appear to be few real-world investors capable of taking advantage of this time-series predictability, especially at the levels of profits suggested by the academic predictability papers.2 As Cochrane (1999) states, “It is uncomfortable to note that fund returns still cluster around the (buy-and-hold) market Sharpe ratio” (68). He suggests that “if the strategy is real and implementable, one must argue that funds simply failed to follow it” (68). Thus there appears to be a large gap between real-time investor performance and the high levels of predictability found in the literature.

We offer an explanation for this performance gap that is based on potential collective data-snooping biases on the part of researchers. This collective snooping may be inherent to the market predictability literature because (1) there is little explicit guidance from theory regarding the identity of the predictive variables used in these studies, hence making it a data-fitting exercise; (2) any new research endeavor is inherently conditioned on the collective knowledge built up to that point; and (3) there is a tendency in the literature and the profession at large to retain the findings that “work” and discard the

ones that do not. Given these issues, it is feasible that a nontrivial proportion of the relations reported in the literature, and accepted as economically meaningful, are simply due to pure luck. As Denton (1985), Lo and MacKinlay (1990), Black (1993a, 1993b), Foster, Smith, and Whaley (1997), Sullivan, Timmermann, and White (1999), Conrad, Cooper, and Kaul (2002), and Ferson, Sarkissian, and Simin (2003) point out, we (usually out of sheer necessity) collectively condition our studies on existing empirical regularities with the unintended consequence of snooping the data. In this paper, we attempt to

gauge the impact of potential data snooping on empirical findings in the return predictability literature that are based on commonly used methodologies and under plausible scenarios of snooping. In the market predictability literature

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Moving Average Rules, Volume and the Predictability of Security Returns with Feedforward Networks : moving_average_rules_volume_and_the_predictability_of_security_returns_with_feedforward_networks.pdf

This paper uses the daily Dow Jones Industrial Average Index from 1963 to 1988 to examine the linear and non-linear predictability of stock market returns with some simple technical trading rules. Some evidence of non- linear predictability in stock market returns is found by using the past buy and sell signals of the moving average rules. In addition, past information on volume improves the forecast accuracy of current returns. The technical trading rules used in this paper are very popular and very simple. The results here suggest that it is worth while to investigate more elaborate rules and the pro®tability of these rules after accounting for transaction costs and brokerage fees.

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Dealing with Predictable Irrationality – Actuarial Ideas to Strengthen Global Financial Risk Management : dealing_with_predictable_irrationality__actuarial_ideas_to_strengthen_global_financial_risk_manageme.pdf

The recent developments in global financial markets have raised serious questions about the management and oversight of the financial services industries, at both the “micro” level for individual entities and at the “macro” level for the system as a whole.

Actuaries are experienced in both measuring and managing risk; although they cannot prevent irrational behaviour, actuarial methods can mitigate its impact and reduce uncertainties. The International Actuarial Association (IAA), representing the global actuarial profession, sees many lessons being learned from this crisis and is suggesting potential reforms, improvements and solutions applicable across the financial services sector. The IAA also believes that the tools and methodologies being developed in the emerging field of Enterprise Risk Management (ERM) will become increasingly important to all financial market participants and their regulatory supervision in the future. The actuarial profession has an important contribution to make in this regard. The concept of ERM is briefly explained at the end of this note.

Necessary initiatives, consistent with the declaration of the G20 on 15 November 2008, include strengthening transparency and accountability, enhancing sound regulation, promoting integrity in financial markets, reinforcing international co-operation and reforming international financial institutions. While supporting such initiatives, this will not be enough, in our view, to prevent future financial crises without additional measures being taken.

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The Efficient Market Hypothesis and Its Critics : the_efficient_market_hypothesis_and_its_critics.pdf

A generation ago, the efŽ cient market hypothesis was widely accepted by academic Žfinancial economists; for example, see Eugene Fama’s (1970) inflential survey article, “Efficient Capital Markets.” It was generally believed that securities markets were extremely efŽficient in reflecting information about individual stocks and about the stock market as a whole. The accepted view was that when information arises, the news spreads very quickly and is incorporated into the prices of securities without delay. Thus, neither technical analysis, which is the study of past stock prices in an attempt to predict future prices, nor even fundamental analysis, which is the analysis of financial information such as company earnings and asset values to help investors select “undervalued” stocks, would enable an investor to achieve returns greater than those that could be obtained by holding a randomly selected portfolio of individual stocks, at least not with comparable risk.

The efficient market hypothesis is associated with the idea of a “random walk,” which is a term loosely used in the finance literature to characterize a price series where all subsequent price changes represent random departures from previous prices. The logic of the random walk idea is that if the  ow of information is unimpeded and information is immediately reflected in stock prices, then tomorrow’s

price change will re ect only tomorrow’s news and will be independent of the price changes today. But news is by definition unpredictable, and, thus, resulting price changes must be unpredictable and random. As a result, prices fully reflect all known information, and even uninformed investors buying a diversifiŽed portfolio at the tableau of prices given by the market will obtain a rate of return as generous as that achieved by the experts.

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Liquidity provision and stock return predictability : liquidity_provision_and_stock_return_predictability.pdf

This paper examines the trading behavior of two groups of liquidity providers (specialists and competing market makers) using a six-year panel of NYSE data. Trades of each group are negatively correlated with contemporaneous price changes. To test for return predictability, we sort stocks into quintiles based on each group’s past trades and then form long-short portfolios. Stocks most heavily bought have significantly higher returns than stocks most heavily sold over the two weeks following a sort. Cross-sectional analysis shows smaller, more volatile, less actively traded, and less liquid stocks more often appear in the extreme quintiles. Time series analysis shows the long-short portfolio returns are positively correlated with a market-wide measure of liquidity. A double sort using past trades of specialists and competing market makers produces a long-short portfolio that earns 88 basis points per week (act as complements). Finally, we identify a ‘‘chain’’ of liquidity provision. Designated market makers (NYSE specialists) initially trade against order flows and prices changes. Specialists later mean revert their inventories by trading with competing market makers who appear to spread trades over a number of days. Alternatively, specialists may trade with competing market makers who arrive to market with delay.