Market Predictability - page 7
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I am curious on what justification do any of us have to have accepted the assumption of price predictability and market inefficiency?
The out-of-sample test period is seven years of five-minute series on three major foreign exchange rates against the US Dollar and one cross-rate. Performance of the real-time trading models is measured by the annualized return, two measures of risk corrected annualized return, deal frequency and maximum drawdown.
The simulated probability distributions of these performance measures are calculated with three popular processes, the random walk, GARCH and AR-GARCH. The null hypothesis that the real-time performances of the foreign exchange series are generated from these traditional processes is tested under the probability distributions of the performance measures. In other words, we compute the probability of the real performance if the distribution was originated by these processes.
All four currencies yield positive annualized returns in the studied sampling period. These annualized returns are net of transaction costs. The results indicate that the excess returns of the real-time trading models, after taking the transaction costs and correcting for market risk, are not spurious. The random walk, GARCH(1,1) and AR-GARCH(1,1) processes are rejected as the data generating mechanisms for the high frequency foreign exchange rates. One important reason for this rejection is the aggregation properties of the GARCH processes. The GARCH process behaves more like a homoskedastic process at lower frequencies. Since the trading frequency is less than two deals per week, the trading model does not pick up the five minute level heteroskedastic structure at the weekly frequency.
The results indicate that the foreign exchange series may possess a multi-frequency conditional mean and conditional heteroskedastic dynamics. The traditional heteroskedastic models fail to capture the entire dynamics by only capturing a slice of this dynamics at a given frequency. Therefore, a more realistic processes for foreign exchange returns should give consideration to the scaling behavior of returns at different frequencies and this scaling behavior should be taken into account in the construction of a representative process.
All right. The market is absolutely predictable. After up is down, down is up after. And so constantly)))
it's ingenious notice))) but seriously, I believe market is really predictable
I am curious on what justification do any of us have to have accepted the assumption of price predictability and market inefficiency?
Based on a large sample of touted stocks listed on the Pink Sheets quotation system and a large sample of spam emails touting stocks, we find that stocks experience a significantly positive return on days prior to heavy touting via spam. Volume of trading responds positively and significantly to heavy touting. For a stock that is touted at some point during our sample period, the probability of it being the most actively traded stock in our sample jumps from 4% on a day when there is no touting activity to 70% on a day when there is touting activity. Returns in the days following touting are significantly negative. The evidence accords with a hypothesis that spammers "buy low and spam high," purchasing penny stocks with comparatively low liquidity, then touting them - perhaps immediately after an independently occurring upward tick in price, or after having caused the uptick themselves by engaging in preparatory purchasing - in order to increase or maintain trading activity and price enough to unload their positions at a profit. We find that prolific spamming greatly affects the trading volume of a targeted stock, drumming up buyers to prevent the spammer's initial selling from depressing the stock's price. Subsequent selling by the spammer (or others) while this buying pressure subsides results in negative returns following touting. Before brokerage fees, the average investor who buys a stock on the day it is most heavily touted and sells it 2 days after the touting ends will lose close to 5.5%. For those touted stocks with above-average levels of touting, a spammer who buys on the day before unleashing touts and sells on the day his or her touting is the heaviest, on average, will earn 4.29% before transaction costs. The underlying data and interactive charts showing price and volume changes are also made available.
improvements over plausible alternatives.
In this paper we introduce a nonparametric estimation method for a large Vector Autoregression (VAR) with time-varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs (FAVAR). When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and large Bayesian VARs with time-varying parameters. The tool can also be used for structural analysis. As an example, we study the time-varying effects of oil price innovations on sectoral U.S. industrial output. We find that the changing interaction between unexpected oil price increases and business cycle fluctuations is shaped by the durable materials sector, rather by the automotive sector on which a large part of the literature has typically focused.