Market Predictability - page 5

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the_market_impact_of_large_trading_orders_-_correlated_order_fl_ow_asymmetric_liquidity_and_efficien.pdf

We study the price change associated with the incremental execution of large trading orders. The heavy tails of large order sizes leads to persistence in the signs of transactions: Buyer initiated transactions tend to be followed by buyer initiated transactions and seller initiated transactions tend to be followed by seller initiated transactions. The resulting predictability in order ow implies that to preserve market efficiency, liquidity must be asymmetric in the sense that trades of the same same sign as the large order generate smaller returns than returns of the opposite sign. The predictability of order ow increases during the execution of a large order, making returns smaller and causing the overall impact to be a concave function of order size. This depends on the information market participants have about order flow. Under assumptions described in the paper, the theory that we develop predicts the functional form of market impact, the degree to which impact is temporary or permanent, and its dependence on trading velocity. We perform empirical tests using data from the London Stock Exchange.
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ATTENTION: Video should be reuploaded
currency_momentum_strategies.pdf

Very good book. Thanks

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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.
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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.
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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
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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
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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

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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
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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.
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