The intra-day performance of market timing strategies and trading systems based on Japanese candlesticks

Abstract

We develop market timing strategies and trading systems to test the intra-day predictive power of Japanese candlesticks at the 5-minute interval on the 30 constituents of the DJIA index. Around a third of the candlestick rules outperform the buy-and-hold strategy at the conservative Bonferroni level. After adjusting for trading costs, however, just a few rules remain profitable. When we correct for data snooping by applying the SSPA test on double-or-out market timing strategies, no single candlestick rule beats the buy-and-hold strategy after transaction costs. We also design fully automated trading systems by combining the best-performing candlestick rules. No evidence of out-performance is found after transaction costs. Although Japanese candlesticks can somewhat predict intra-day returns on large US caps, we show that such predictive power is too limited for active portfolio management to outperform the buy-and-hold strategy when luck, risk, and trading costs are correctly measured.     

Aggregate insider trading: Contrarian beliefs or superior information?

We decompose realized market returns into expected return, unexpected cash-flow news and unexpected discount rate news to test the relation between aggregate market returns and aggregate insider trading. We find that (1) the predictive ability of aggregate insider trading is much stronger than what was reported in earlier studies, (2) aggregate insider trading is strongly related to unexpected cash-flow news, (3) market expectations do not cause insider trading contrary to what others have documented, and (4) aggregate insider trading in firms with high information uncertainty is more likely to be associated with contrarian investment strategy. These results strongly suggest that the predictive ability of aggregate insider trading is because of insider’s ability to predict future cash-flow news rather than from adopting a contrarian investment strategy. These results hold even after we control for non-informative trades and information uncertainty.     

Discount rate changes,stock market returns,volatility, and trading volume: Evidence from intraday data and implications for market efficiency

We examine the effect of discount rate changes on stock market returns, volatility, and trading volume using intraday data. Equity returns generally respond negatively and significantly to the unexpected announcements; however, the effect of expected changes on equity returns is insignificant. Furthermore, our results indicate that equity prices respond to announcements within the trading period/hour after the information release. An indication of a return reversal is too small to cover the full transaction costs. Unexpected discount rate changes also contribute to higher market volatility although the volatility is short-lived. Similarly, unexpected changes in discount rates induce larger trading volume while expected changes do not. Abnormal trading volume occurs only in period t. Our results also support the notion that unexpected changes in the discount rates impact market returns irrespective of the Federal Reserve operating procedures.     

Predicting bear and bull stock markets with dynamic binary time series models

Despite the voluminous empirical research on the potential predictability of stock returns, much less attention has been paid to the predictability of bear and bull stock markets. In this study, the aim is to predict U.S. bear and bull stock markets with dynamic binary time series models. Based on the analysis of the monthly U.S. data set, bear and bull markets are predictable in and out of sample. In particular, substantial additional predictive power can be obtained by allowing for a dynamic structure in the binary response model. Probability forecasts of the state of the stock market can also be utilized to obtain optimal asset allocation decisions between stocks and bonds. It turns out that the dynamic probit models yield much higher portfolio returns than the buy-and-hold trading strategy in a small-scale market timing experiment.     

The predictability of real estate returns and market timing

Recent evidence suggests that all asset returns are predictable to some extent with excess returns on real estate relatively easier to forecast. This raises the issue of whether we can successfully exploit this level of predictability using various market timing strategies to realize superior performance over a buy-and-hold strategy. We find that the level of predicability associated with real estate leads to moderate success in market timing, although this is not necessarily the case for the other asset classes examined in general. Besides this, real estate stocks typically have higher trading profits and higher mean risk-adjusted excess returns when compared to small stocks as well as large stocks and bonds even though most real estate stocks are small stocks.     

Market Sentiment and Investor Overreaction: Evidence from New York Listed Asian Country Exchange Traded Funds

This article investigates Asian Country Exchange-Traded Fund (ETF) price deviation with underlying due to market sentiment. By implementing a dynamic contrarian trading strategy and a buy-and-hold strategy, this article finds that significant abnormal excess trading profit can be generated by capitalizing on the overnight price reversion. The excess return generated by the dynamic strategy over buy-and-hold separates the influence of market sentiment to ETF price deviation from fundamental movements. By studying the relations between variations of the excess returns and market sentiment, the article finds that the ETF price deviation is highly influenced by market sentiment and the effect exacerbates during financial crisis and distress.     

Japan and Hong Kong Exchange-Traded Funds (ETFs): Discounts, Returns, and Trading Strategies

Foreign exchange-traded funds (ETFs) trade on U.S. exchanges but provide broad exposure to foreign markets. ETFs are designed to minimize the deviation between price and value of the underlying securities. However, nonoverlapping trading hours between the United States and many foreign markets inhibit this mechanism. The data for Japan and Hong Kong iShares show that deviations exist between the ETF price and the value of the underlying securities. The deviations are positively related to subsequent ETF returns creating potential profit opportunities. A simple trading rule based on this observation produces impressive gross returns when compared to a buy-and-hold strategy.     

An analysis of trading strategies in eleven European stock markets

Abstract

In recent years, the validity of the weak form efficient market hypothesis (EMH) has been called into question as several studies have uncovered evidence that technical trading rules have predictive ability with respect to both developed and emerging stock market indices. This study analyses the forecasting power of 2 of the most popular trading rules using index data for a selection of 11 European stock markets over the January 1991 to December 2000 period. The findings indicate that the emerging markets included in this paper are informationally inefficient; these markets displayed some degree of predictability in their share returns, although the developed markets did not. Furthermore, the results point to large differences in the performance of the rules examined; while small size filters consistently outperformed the buy-and-hold strategy in the emerging markets examined even after the consideration of transaction costs, the performance of the moving average rules was erratic and varied dramatically from market to market.     

Relative Excess Returns

Building on a no-arbitrage relationship suggested by Clare, Thomas and Wickens (1994) between the returns on equity, bonds and treasury bills, this paper develops what is termed a 'relative excess returns' approach to the understanding of movements in equity prices. This no-arbitrage relationship is used to derive an explicit measure of excess returns, which incorporates both the excess returns to equity and bonds while netting out any unprofitable (i.e. market efficient) return predictability caused by time variation in the treasury bill rate. This measure can be related to a series of observable variables in a consistent manner and used to construct a trading rule aimed at forecasting excess returns. In a series of empirical experiments, this trading rule appears to be more 'profitable' than both the rule suggested by Clare et al. (1994) and the gilt-equity yield ratio rule (used by many UK analysts to guide investment decisions), and outperforms the strategy of 'buy and hold equity'. More generally, the analysis provides support for the existence of predictable excess returns — returns which cannot be attributed to time-varying excess returns — and for the inefficient market explanation of predictable returns.     

Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500

This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the algorithm efficiently determines the optimal non-linear mapping and the corresponding lead–lag structure between two time series. Afterwards, this study explores the use of optimal causal paths as a means for identifying promising stock pairs and for generating buy and sell signals. For this purpose, the established trading strategy exploits information about the leading stock to predict future returns of the following stock. The value-add of the proposed framework is assessed by benchmarking it with variants relying on classic similarity measures and a buy-and-hold investment in the S&P 500 index. In the empirical back-testing study, the trading algorithm generates statistically and economically significant returns of 54.98% p.a. and an annualized Sharpe ratio of 3.57 after transaction costs. Returns are well superior to the benchmark approaches and do not load on any common sources of systematic risk. The strategy outperforms in the context of cryptocurrencies even in recent times due to the fact that stock returns contain substantial information about the future bitcoin returns.     

The role of institutional investors in seasoned equity offerings

Do institutional investors possess private information about seasoned equity offerings (SEOs)? If so, do they use this private information to trade in a direction opposite to this information (a manipulative trading role) or in the same direction (an information production role)? We use a large sample of transaction-level institutional trading data to distinguish between these two roles of institutional investors. We explicitly identify institutional SEO allocations for the first time in the literature. We analyze the consequences of the private information possessed by institutional investors for SEO share allocation, institutional trading before and after the SEO and realized trading profitability, and the SEO discount. We find that institutions are able to identify and obtain more allocations in SEOs with better long-run stock returns, they trade in the same direction as their private information, and their post-SEO trading significantly outperforms a naive buy-and-hold trading strategy. Further, more pre-offer institutional net buying and larger institutional SEO allocations are associated with a smaller SEO discount. Overall, our results are consistent with institutions possessing private information about SEOs and with an information production instead of a manipulative trading role for institutional investors in SEOs.     

Converse trading strategies,intrinsic noise and the stylized facts of financial markets

This paper proposes a simple asset pricing model with three groups of traders: chartists who believe in the persistence of bull and bear markets, fundamentalists who bet on a reduction of the observed mispricing, and investors who follow a buy-and-hold strategy. The innovative feature of the model concerns the frequency of trading: rather than remaining constant over time, each agent in a group is only assumed to become active with a certain probability over a given market period. Depending on the trading strategy, part of this elementary kind of intrinsic noise is additive and another part is multiplicative. Using bootstrap and Monte Carlo methods, it is demonstrated that this combination can contribute to explaining the stylized facts of the daily returns on financial markets, such as volatility clustering, fat tails, and the autocorrelation patterns.     

Dynamic portfolio optimization across hidden market regimes

Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. In this article, model predictive control (MPC) is used to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using MPC when estimates of future returns are updated every time a new observation becomes available, since the optimal control actions are reconsidered anyway. MPC outperforms a static decision rule for changing the allocation and realizes both a higher return and a significantly lower risk than a buy-and-hold investment in various major stock market indices. This is after accounting for transaction costs, with a one-day delay in the implementation of allocation changes, and with zero-interest cash as the only alternative to the stock indices. Imposing a trading penalty that reduces the number of trades is found to increase the robustness of the approach.     

On the efficiency of the gold market: Results of a real-time forecasting approach

Using a real-time forecasting approach, we study whether publicly available information on a large set of financial and macroeconomic variables help in forecasting out-of-sample monthly excess returns on investing in gold. The real-time forecasting approach accounts for the fact that an investor must reach an investment decision in real time under uncertainty concerning the optimal forecasting model. The real-time forecasting approach also accounts for the possibility that the optimal forecasting model may change over time. We account for transaction costs and show that using forecasts implied by the real-time forecasting approach to set up a simple trading rule does not necessarily lead to a superior performance relative to a buy-and-hold strategy, implying that the gold market is informationally efficient with respect to the predictor variables that we study in this research.     

Return enhancement trading strategies for size based portfolios

Recent theoretical work suggests that definitions of market efficiency that allow for the possibility of time-varying risk-premia will generally lead to return sign predictability. Consistent with this theory, we show that a logit model based on the lagged value of the market risk premium is useful for successfully predicting the return sign for CRSP small decile portfolio returns, but not large ones. We additionally employ this model in market timing simulations of micro-cap mutual funds in which investment can actually be made. The results indicate that a market-timing strategy based on our return-sign forecasting model outperforms a buy-and-hold strategy for 13 of 14 micro-cap funds studied. On average, the buy-and-hold strategy produces an average compound return of 11.98% per annum versus an average of 16.60% for the market-timing strategy. Nevertheless, trading restrictions make the return-sign forecasting model more practical to employ by the micro-cap fund portfolio manager rather than the individual fund investor.

Dynamic trading with uncertain exit time and transaction costs in a general Markov market

This paper investigates a dynamic trading problem with transaction cost and uncertain exit time in a general Markov market, where the mean vector and covariance matrix of returns depend on the states of the stochastic market, and the market state is regime switching in a time varying state set. Following the framework proposed by Gârleanu and Pedersen (2013), the investor maximizes his or her multi-period mean–variance utility, net of quadratic transaction costs capturing the linear price impact where trades lead to temporary linear changes in prices. The explicit expression for the optimal strategy is derived by using matrix theory technique and dynamic programming approach. Finally, numerical examples are provided to study the effects of transition cost and exit probability on the wealth process, the trading strategy, turnover rate and the total transaction cost.     

Predictability of Stock Returns: Robustness and Economic Significance

This article examines the robustness of the evidence on predictability of U.S. stock returns, and addresses the issue of whether this predictability could have been historically exploited by investors to earn profits in excess of a buy-and-hold strategy in the market index. We find that the predictive power of various economic factors over stock returns changes through time and tends to vary with the volatility of returns. The degree to which stock returns were predictable seemed quite low during the relatively calm markets in the 1960s, but increased to a level where, net of transaction costs, it could have been exploited by investors in the volatile markets of the 1970s.     

BUSINESS CYCLES AND STOCK MARKET RETURNS: EVIDENCE USING INDUSTRY-BASED PORTFOLIOS

Much evidence has emerged recently that suggests stock returns are predictable. In representative agent consumption-based asset pricing models, asset returns are related to aggregate output and consumption through changes in the intertemporal marginal rate of substitution. An alternative view is that the amount of variation required in the intertemporal marginal rate of substitution is too large to be rationally explained. We shed further light on this debate by investigating whether the stock returns of certain sectors of the economy can predict future market returns even after controlling for the information contained in the aggregate market index. In the consumption-based models, aggregate output and consumption affect the discount rates of all assets synchronously; no particular sectoral return should have any more predictive ability than the others. We find evidence that the stock returns of five industry-based portfolios have significant information about future market returns that is not in the market index. This stylized empirical result is not consonant with existing models relating output to stock returns.     

Trading of foreign investors and stock returns in an emerging market - Evidence from Vietnam

This article investigates the impact of foreign investors' trading on stock returns in Vietnam, a key emerging market. We utilize a time series data set of foreign investors' trading volume and market returns of the Ho Chi Minh City stock exchange over an extended time frame before and after global financial crisis. The results indicate that foreign investors are positive feedback traders in Vietnam stock market. The findings also reveal the timing ability and trading strategy of foreign investors. The paper offers strong implications for market participants and portfolio investment.