Profitable Trading Strategies Research Articles

A link mining algorithm for earnings forecast and trading

The objective of this paper is to present and discuss a link mining algorithm called CorpInterlock and its application to the financial domain. This algorithm selects the largest strongly connected component of a social network and ranks its vertices using several indicators of distance and centrality. These indicators are merged with other relevant indicators in order to forecast new variables using a boosting algorithm. We applied the algorithm CorpInterlock to integrate the metrics of an extended corporate interlock (social network of directors and financial analysts) with corporate fundamental variables and analysts' predictions (consensus). CorpInterlock used these metrics to forecast the trend of the cumulative abnormal return and earnings surprise of S&P 500 companies. The rationality behind this approach is that the corporate interlock has a direct effect on future earnings and returns because these variables affect directors and managers' compensation. The financial analysts engage in what the agency theory calls the "earnings game": Managers want to meet the financial forecasts of the analysts and analysts want to increase their compensation or business of the company that they follow. Following the CorpInterlock algorithm, we calculated a group of well-known social network metrics and integrated with economic variables using Logitboost. We used the results of the CorpInterlock algorithm to evaluate several trading strategies. We observed an improvement of the Sharpe ratio (risk-adjustment return) when we used "long only" trading strategies with the extended corporate interlock instead of the basic corporate interlock before the regulation Fair Disclosure (FD) was adopted (1998---2001). There was no major difference among the trading strategies after 2001. Additionally, the CorpInterlock algorithm implemented with Logitboost showed a significantly lower test error than when the CorpInterlock algorithm was implemented with logistic regression. We conclude that the CorpInterlock algorithm showed to be an effective forecasting algorithm and supported profitable trading strategies.

Market Microstructure Patterns Powering Trading and Surveillance Agents

Market Surveillance plays important mechanism roles in constructing market models. From data analysis perspective, we view it valuable for smart trading in designing legal and profitable trading strategies and smart regulation in maintaining market integrity, transparency and fairness. The existing trading pattern analysis only focuses on interday data which discloses explicit and high-level market dynamics. In the mean time, the existing market surveillance systems available from large exchanges are facing crucial challenges of diversified, dynamic, distributed and cyber-based misuse, mis-disclosure and misdealing of information, announcement and orders in one market or crossing multiple markets. Therefore, there is a crucial need to develop innovative and workable methods for smart trading and surveillance. To deal with such issues, we propose the innovative concept microstructure pattern analysis and corresponding approaches in this paper. Microstructure pattern analysis studies trading behaviour patterns of traders in market microstructure data by utilizing market microstructure knowledge. The identified market microstructure patterns are then used for powering market trading and surveillance agents for automatically detecting/designing profitable and legal trading strategies or monitoring abnormal market dynamics and trader's behaviour. Such trading/surveillance agent-driven market trading/surveillance systems can greatly enhance the analytical, discovery and decision-support capability of market trading/surveillance than the current predefined rule/alert-based systems.

Environmental Stressors, Mood, and Trading Decisions: Evidence from Ambient Air Pollution

Drawing upon the evidence produced in medicine and psychology, we show that air-qualityinduced mood changes affect the decisions of the trading floor community and, ultimately, stock returns. We initially take advantage of a natural experiment involving the Milan Stock Exchange, and document the existence of a pollution/mood effect during the period in which trading was conducted on the floor, whereas no evidence is detected once the floor community is replaced by a computerized and decentralized trading system. Subsequently, by tracking the air pollution concentrations prevailing near the sites that host a sample of major Stock Exchanges, which act as a proxy for trader mood, we find that such measures display a systematic negative relationship with the performance of the corresponding market indices, provided the Exchange at hand employs a trading floor. The empirical regularities that we identify are inconsistent with the Efficient Market Theory, as we demonstrate that there exist some unexploited profitable trading strategies based on public air pollution data.

Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm

Universal compression algorithms can detect recurring patterns in any type of temporal data--including financial data--for the purpose of compression. The universal algorithms actually find a model of the data that can be used for either compression or prediction. We present a universal Variable Order Markov (VOM) model and use it to test the weak form of the Efficient Market Hypothesis (EMH). The EMH is tested for 12 pairs of international intra-day currency exchange rates for one year series of 1, 5, 10, 15, 20, 25 and 30 min. Statistically significant compression is detected in all the time-series and the high frequency series are also predictable above random. However, the predictability of the model is not sufficient to generate a profitable trading strategy, thus, Forex market turns out to be efficient, at least most of the time.

Tactical Allocation in Commodity Futures Markets: Combining Momentum and Term Structure Signals

This paper examines the combined role of momentum and term structure signals for the design of profitable trading strategies in commodity futures markets. With significant annualized alphas of 10.14% and 12.66% respectively, the momentum and term structure strategies appear profitable when implemented individually. With an abnormal return of 21.02%, a novel double-sort strategy that exploits both momentum and term structure signals clearly outperforms the single-sort strategies. This double-sort strategy can additionally be utilized as a portfolio diversification tool. Interestingly, the abnormal performance of the double-sort portfolios cannot be explained by a lack of liquidity or data mining and is robust to transaction costs and to different specifications of the risk-return trade-off.

November Effect: An Example of Calender Anomaly in Indian Stock Market

This study investigates the month-of-the-year in Indian stock in recent years using monthly data on Indian market index and some other sectoral indices. It accounts for the time-varying volatility of the Indian stock market, at both the market and sectoral level, using the GARCH model and GARCH-M model. This study looks for the presence of calendar anomaly with asymmetric market reactions using TARCH model. It confirms the presence of a significant November effect at both the levels that cannot be explained by the time varying volatility of stock return. A similar trend also persists at the sectoral level. Presence of such seasonal anomaly in the form of a November effect could have significant bearing for the policy makers as well as for individual investors for designing profitable trading strategies. The study attempts further to account for such seasonality in stock return in Indian context.

The Diminishing Liquidity Premium

Previous evidence suggests that less liquid stocks entail higher average returns. Using NYSE data, we present evidence that both the sensitivity of returns to liquidity and liquidity premia have significantly declined over the past four decades to levels that we cannot statistically distinguish from zero. Furthermore, the profitability of trading strategies based on buying illiquid stocks and selling illiquid stocks has declined over the past four decades, rendering such strategies virtually unprofitable. Our results are robust to several conventional liquidity measures related to volume. When using liquidity measure that is not related to volume, we find just weak evidence of a liquidity premium even in the early periods of our sample. The gradual introduction and proliferation of index funds and exchange traded funds is a possible explanation for these results.

Portfolio Returns and Target Prices

In this paper we examine the profitability of trading strategies based on target prices embedded in equity research reports. We adopt target prices as information signals to build our portfolio strategy. At the first stage, we exploit implicit returns (difference between the current price and the target price) as a buying or short selling signal to open every transaction in the portfolio. Next, we use raw and adjusted target prices as a closing transaction signal. We build three different strategies to verify if the overshooting of target prices revealed by several studies could affect the profitability of a trading strategy based on these target prices. We have formed four portfolios within every strategy to verify whether analysts' target prices have any investment value. We find that all strategies deliver positive abnormal returns against the market. In particular, the highest and lowest implicit return classes exhibit the highest stock returns and abnormal returns. In the last part control these returns for systematic risk, factors through the CAPM and the Fama & French three factor model. The positive performances of our strategies persist and, in particular, our strategies beat the market with consistent abnormal returns in all the four implicit return classes analyzed. From the three factors equation, it seems that analysts tend to advise buying stocks with risk above the average market risk, with growth profile and with large market value. Conversely analysts advise selling value stocks and those with a negative beta.

Suppressed Negative Information and Future Underperformance*

I present evidence of inefficient information processing in equity markets by documenting that negative information withheld by securities analysts is incorporated in stock prices with a significant delay. I estimate the extent of the withheld negative information based on the proportion of analysts who stop revising their annual earnings forecasts. This measure predicts negative earnings surprises and negative price reaction around earnings announcements. It could also be used to generate profitable trading strategies. I show that institutions tend to sell their stock holdings as my measure of unreported negative news increases, thus ameliorating the mispricing.

Random walk currency futures profits revisited

PurposeRecent research indicates that the random walk hypothesis (RWH) approximately describes the behavior of major dollar exchange rates during the post‐1973 float. The present analysis seeks to examine the profitability of currency futures trading rules that assume that spot exchange rates can be adequately modeled as a driftless random walk.Design/methodology/approachTwo random walk currency futures trading rules are simulated over all available data from the period 1984‐2003. In both cases, the investor buys currencies selling at a discount and sells those selling at a premium, as the RWH implies. The two rules differ only in the way they allocate the hypothetical investor's resources among long and short foreign currency positions.FindingsResults show that an investor who used these trading strategies over the past decade would have enjoyed large cumulative gains, although periods of profit were interrupted by periods of substantial loss.Research limitations/implicationsThe findings encourage the hope that profitable random‐walk‐based strategies for currency futures trading can be devised. The simulation results have important implications for those willing to hedge, borrowers, and speculators.Originality/valueThis paper provides evidence that purchasing futures contracts on currencies priced at a discount and selling futures contracts priced at a premium has generally been a profitable trading strategy during the last two decades of floating exchange rates.

Trading with Asymmetric Volatility Spillovers

Abstract: We study the profitability of trading strategies based on volatility spillovers between large and small firms. By using the Volatility Impulse‐Response Function of Lin (1997) and its extensions, we detect that any volatility shock coming from small companies is important to large companies, but the reverse is only true for negative shocks coming from large firms. To exploit these asymmetric patterns in volatility, different trading rules are designed based on the inverse relationship existing between expected return and volatility. We find that most strategies generate excess after‐transaction cost profits, especially after very bad news and very good news coming from large or small firm markets. These results are of special interest because of their implications for risk and portfolio management.

Time-Changing Alpha? The Case of Australian International Mutual Funds

Tests for active management inevitably focus on long periods. Yet, implicit in these tests is the assumption that active management generates a stable excess return. We argue that this assumption is not appropriate for active management where the emphasis is on identifying profitable trading strategies. Consistent with this conjecture, we find evidence of time-changing alpha using a sample of Australian international funds over the period from July 1995 to January 2005. Regardless, few international funds show consistent positive excess returns over the period.

Suppressed Negative Information and Future Underperformance

We present evidence of inefficient information processing in equity markets by documenting that negative information withheld by securities analysts is reflected in stock prices with a significant delay. We estimate the extent of the withheld negative information based on the proportion of analysts who stop revising their annual earnings forecasts. This measure predicts negative earnings surprises and negative price reaction around earnings announcements. It could also be used to generate profitable trading strategies. We show that institutions tend to sell their stock holdings as our measure of unreported negative news increases, thus ameliorating the mispricing.

Detecting market transitions and energy futures risk management using principal components

An empirical approach to analysing the forward curve dynamics of energy futures is presented. For non-seasonal commodities—such as crude oil—the forward curve is well described by the first three principal components: the level, slope and curvature. A principal component indicator is described that detects transitions between the two fundamental market states remarkably well. For seasonal commodities—such as electricity and natural gas—it is shown how to extract the seasonal component from the forward curve. The principal component indicator can then be applied to the de-seasoned forward curve to detect significant price deviations that may support profitable trading strategies. A principal component approach to forward curve modelling is applied to computing portfolio value-at-risk. This approach is combined with a new two-step resampling procedure to improve value-at-risk estimates.

Equity Returns at the Turn of the Month: Trading Strategies and Implications for Investors and Managers

We propose to extend and complete the attached study of the effect in equity returns in two ways. First, we propose to study whether the effect is more pronounced among certain industries where industries are determined by SIC codes and/or the Fama-French industry classifications and/or whether the effect is more pronounced in certain time periods especially periods of 'high' investor sentiment. Second, we propose to investigate whether the effect can be used to generate a profitable trading strategy after trading costs. This latter investigation will examine three low cost strategies: 1. a strategy of switching between an equity fund and a bond fund of the same mutual fund family; 2. a strategy of buying and selling exchange traded funds (ETFs), either specific styles of ETFs or market-based ETFs; and 3. a strategy of buying and selling equity index futures. We propose to consider the implications of these for fund managers especially funds that might be exposed to being picked off by a turn-of-the-month trading strategy. The turn-of-the-month effect in U.S. equity returns was initially identified by Ariel (1987), who studies CRSP market returns over the period 1963-1982, and Lakonishok and Smidt (LS whereas over the other 16 trading days, it is -0.001%. When our results are combined with those of LS it is not concentrated at calendar year-ends or quarter-ends; and it is not confined to the US: of the 34 countries we study, the effect occurs in 30 of them. Further the effect is not due to risk as measured by standard deviation of returns. Finally, we find that the effect is not due to increased buying pressure measured by trading volume and net flows to mutual funds. We propose to extend and complete this study as described above.

Time to Maturity Volatility: An Application to Index Derivatives

The basic assumption of the Black-Scholes option pricing is that volatility is constant over the time to maturity of the option. We consider how the estimation of volatility is affected by the time to maturity. In particular, we consider the empirical distribution of volatility as a function of the time to maturity and propose a threshold estimator based on the reverting behavior of implied volatility towards the median of the empirical volatility distribution. This estimator is compared with implied and historical volatility estimators in a forecasting study of the Australian SPI 200 index futures contract. The new estimator generates smaller forecast errors of realized volatility and is the basis of a profitable trading strategy.

Trading in the Australian Foreign Exchange Market

The market for for foreign exchange is the most heavily traded financial market. Theoretically exchange exchange rate movements are determined by economic fundamentals, such as inflation and interest rates, which influence the supply and demand for currencies. However, empirical models of exchange rate determination based on these fundamentals have not been very successful in predicting exchange rate movements, especially over the short run. Furthermore, market practitioners argue that they have successfully developed profitable trading strategies, which do not rely on an analysis of economic fundamentals. A reason for the poor performance of econometric models and trading strategies based on fundamental analysis could be the attitudes and behaviour of practitioners trading in the foreign exchange market. In order to obtain information about trading behaviour in the Australian foreign exchange market dealers authorised to trade in this market were approached in a survey for their views on market behaviour and trading strategies. This paper provides a detailed analysis of the responses to the survey questions.

Chronic Bias in Earnings Forecasts

Whatever the source or explanation of bias in forecasts of company earnings, if such bias persists, it is potentially discoverable and exploitable by investors. This research addresses (1) whether characterizing forecasts as if they were a homogeneous group with respect to bias is accurate or useful and (2) whether a long-term record of forecast errors contains information useful in predicting subsequent errors. We found that earnings forecasts are heterogeneous with respect to direction and degree of bias. We also found evidence of extremes in optimism and pessimism and that extreme errors tend to persist in the same direction, which suggests certain potentially profitable trading strategies.

On the effectiveness of the anti-gaming policy between the day-ahead and real-time electricity markets in The Netherlands

In this paper, we study the linkage between two related markets for electricity in The Netherlands: the day-ahead market and the real-time market. The Dutch regulator wants to prevent trading across these two markets and has set up a dual pricing system for this purpose. In this paper, we test the effectiveness of this policy by studying the ex post profitability of trading strategies spanning the two markets over various time segments. Our results show that profits generated by these strategies are rarely positive on average and always characterized by very large potential losses, which dwarf the mean profit when the latter is positive.

Trading with Asymmetric Volatility Spillovers

We study the profitability of trading strategies based on volatility spillovers between large and small firms. By using the Volatility Impulse-Response Function of Lin (1997) and its extensions, we detect that any volatility shock coming from small companies is important to large companies, but the reverse is only true for negative shocks coming from large firms. To exploit these asymmetric patterns in volatility, different trading rules have been designed based on the inverse relationship existing between expected return and volatility. We find that most strategies generate excess after-transaction profits, especially after very bad news and very good news coming from large or small firm markets. These results are of special interest because of implications for risk and portfolio management.