Market Predictability - page 5

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black_swans_and_market_timing_-_how_not_to_generate_alpha.pdf

Do investors obtain their long term returns smoothly and steadily over time, or is their long term performance largely determined by the return of just a few outliers? How likely are investors to successfully predict the best days to be in and out of the market? The evidence from 15 international equity markets and over 160,000 daily returns indicates that a few outliers have a massive impact on long term performance. On average across all 15 markets, missing the best 10 days resulted in portfolios 50.8% less valuable than a passive investment; and avoiding the worst 10 days resulted in portfolios 150.4% more valuable than a passive investment. Given that 10 days represent less than 0.1% of the days considered in the average market, the odds against successful market timing are staggering.
 

learning_in_financial_markets.pdf

We survey the recent literature on learning in financial markets. Our main theme is that many financial market phenomena that appear puzzling at first sight are easier to understand once we recognize that parameters in financial models are uncertain and subject to learning. We discuss phenomena related to the volatility and predictability of asset returns, stock price bubbles, portfolio choice, mutual fund flows, trading volume, and firm profitability, among others.
 

a_practical_guide_to_quantitative_portfolio_trading.pdf

We discuss risk, preference and valuation in classical economics, which led academics to develop a theory of market prices, resulting in the general equilibrium theories. However, in practice, the decision process does not follow that theory since the qualitative aspect coming from human decision making process is missing. Further, a large number of studies in empirical finance showed that financial assets exhibit trends or cycles, resulting in persistent inefficiencies in the market, that can be exploited. The uneven assimilation of information emphasised the multifractal nature of the capital markets, recognising complexity. New theories to explain financial markets developed, among which is a multitude of interacting agents forming a complex system characterised by a high level of uncertainty. Recently, with the increased availability of data, econophysics emerged as a mix of physical sciences and economics to get the best of both world, in view of analysing more deeply assets' predictability. For instance, data mining and machine learning methodologies provide a range of general techniques for classification, prediction, and optimisation of structured and unstructured data. Using these techniques, one can describe financial markets through degrees of freedom which may be both qualitative and quantitative in nature. In this book we detail how the growing use of quantitative methods changed finance and investment theory. The most significant benefit being the power of automation, enforcing a systematic investment approach and a structured and unified framework. We present in a chronological order the necessary steps to identify trading signals, build quantitative strategies, assess expected returns, measure and score strategies, and allocate portfolios. Number of Pages in PDF File: 842
 

efficiency_in_foreign_exchange_markets.pdf

A quantitative check of weak efficiency in US dollar/German mark exchange rates is developed using high frequency data. We show the existence of long term return anomalies. We introduce a technique to measure the available information and show it can be profitable following a particular trading rule. Number of Pages in PDF File: 21
 

technical_market_indicators_-_an_overview.pdf

Current evidence on the predictability of technical analysis largely concentrates on price-based technical indicators such as moving averages rules and trading range breakout rules. In contrast, the predictability of widely used technical market indicators such as advance/decline lines, volatility indices, and short-term trading indices has drawn limited attention. Although some market indicators have also become popular sentiment proxies in the behavioral finance field to predict returns, the results generally rely on using just one or a few indicators at a time. This approach raises the risk of data snooping, since so many proxies are proposed. We review and examine the profitability of a wide range of 93 market indicators. We give these technical market indicators the benefit of the doubt, but even then we find little evidence that they predict stock market returns. This conclusion continuously holds even if we allow predictability to be state dependent on business cycles or sentiment regimes.

Number of Pages in PDF File: 64

 

intraday_momentum_-_the_first_half-hour_return_predicts_the_last_half-hour_return.pdf

Based on high frequency data of the S&P 500 ETF from 1993-2013, we document an intraday momentum pattern: the first half-hour return on the market predicts the last half-hour return. The predictability, both statistically and economically significant, is stronger on more volatile days, on higher volume days, on recession days, and on major macroeconomic news release days. This intraday momentum is also strong for ten other most actively traded domestic and international ETFs, and two major international equity index futures. Theoretically, the intraday momentum is consistent with the trading behavior of informed traders. Number of Pages in PDF File: 44
 

polynomial_variation_vix_decomposition_and_tail_risk_premium.pdf

We identify the VIX index is innately a risk-neutrally forward-looking measure of the polynomial (not quadratic) variation of market returns. Correspondingly, we define the realized VIX (RVIX) as a physically conditional measure of the polynomial variation that captures not only the realized variance but the entire realized jump-tail variability. The VIX risk premium (i.e., squared VIX minuses RVIX) thus compensates jointly the risk of stochastic volatility and that of jump and tail. The difference between the risk-premium of VIX and that of variance (derived from the quadratic variation) further quantifies the compensation for the tail (fear) risk. Consequently, the squared VIX index can be decomposed into four fundamentally different components: the realized variance (RV), the variance risk premium (VRP*), the realized tail (RT), and the tail risk premium (TRP), respectively. The empirical results reveal that VRP*, RT, and TRP help predict future market returns.
 

uncloaking_cape_-_a_new_look_at_an_old_valuation_ratio.pdf

Professor Robert Shiller’s Cyclically Adjusted Price-Earnings (CAPE) Ratio has proven to be a powerful descriptor, as well as a useful predictor, of long-term equity returns in the United States and some global markets. In recent years, though, it has been criticized for being overly pessimistic about the prospects for equity returns, its lack of robustness to distortions in corporate earnings, and for overstating the predictability of returns at long horizons on account of overlapping observations and endogeneity, particularly when estimated using Ordinary Least Squares (OLS). In this paper, we explore various definitions of CAPE, present new construction techniques that make it robust to a wide range of accounting and index construction biases as well as to changing fundamentals in equity markets, and evaluate its forecasts using econometric methods that account for endogeneity and overlapping observations. We show that most of these enhancements have a minimal impact on CAPE for the US equity market, but can prove useful in smaller markets and in markets that have experienced significant dislocations. We also show that certain accounting flow variables such as cash flow and revenues can be useful supplements to earnings and cyclically adjusted earnings. We use these techniques to derive a robust estimate of the expected return of equities in the U.S., and show that it is currently on the order of 6% per annum.
 

comparing_the_market_risk_premia_in_jse_and_nyse_equity_markets.pdf

This paper examines the evidence regarding predictability in the market risk premium using artificial neural networks (ANNs), namely the Elman Network (EN) and the Higher Order Neural network (HONN), univariate ARMA and exponential smoothing techniques, such as Single Exponential Smoothing (SES) and Exponentially Weighted Moving Average (EWMA).

The contribution of this paper is the inclusion of the South African market risk premium to the forecasting exercise and its direct comparison with US forecasting results. The market risk premium is defined as the expected rate of return on the market portfolio in excess of the short-term interest rate for each market. All data are taken from January 2007 till December 2014 on a daily basis.

Elman networks provide superior results among the tested models in both insample and out-of sample periods as well as among the tested markets. In general, neural networks beat the naive benchmark model and achieve to perform better than the rest of their linear tested counterparts.

The forecasting models successfully capture patterns in the data that improve the forecasting accuracy of the tested models. Therefore, they can be applied to trading and investment purposes.
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