Market Predictability - page 7

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John Seekers
793
John Seekers  
This paper attempts to identify implicit exchange rate regimes for the Yen/Dollar exchange rate. To that end, we apply a sequential procedure that considers both the dynamics of exchange rates and central bank interventions to data covering the period from 1971 to 2003. Our results would suggest that implicit bands existed in two subperiods: April-December 1980 and March-December 1987, the latter coinciding with the Louvre Accord. Furthermore, the study of the credibility of such implicit bands indicates the high degree of confidence attributed by economic agents to the evolution of the Yen/Dollar exchange rate within the detected implicit band rate, thus lending further support to the relevance of such implicit bands.
Garry119
136
Garry119  
All right. The market is absolutely predictable. After up is down, down is up after. And so constantly)))
John Seekers
793
John Seekers  
Under minimal assumptions finite sample confidence bands for quantile regression models can be constructed. These confidence bands are based on the "conditional pivotal property" of estimating equations that quantile regression methods aim to solve and will provide valid finite sample inference for both linear and nonlinear quantile models regardless of whether the covariates are endogenous or exogenous. The confidence regions can be computed using MCMC, and confidence bounds for single parameters of interest can be computed through a simple combination of optimization and search algorithms. We illustrate the finite sample procedure through a brief simulation study and two empirical examples: estimating a heterogeneous demand elasticity and estimating heterogeneous returns to schooling. In all cases, we find pronounced differences between confidence regions formed using the usual asymptotics and confidence regions formed using the finite sample procedure in cases where the usual asymptotics are suspect, such as inference about tail quantiles or inference when identification is partial or weak. The evidence strongly suggests that the finite sample methods may usefully complement existing inference methods for quantile regression when the standard assumptions fail or are suspect.
whisperer
1881
whisperer  
AngelGirl:
I am curious on what justification do any of us have to have accepted the assumption of price predictability and market inefficiency?
Did you try reading anything about it? Might help your "curiosity"
John Seekers
793
John Seekers  
Real-time trading models use high frequency live data feeds and their recommendations are transmitted to the traders through data feed lines instantaneously. In this paper, a widely used real-time trading model is considered as a tool to evaluate the statistical properties of foreign exchange rates. This is done by comparing the out-of-sample results of the trading model to those obtained by feeding the algorithm with data simulated from theoretical processes fitted to the real data.

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.
Karaduman
34
Karaduman  
Garry119:
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
yenPound
290
yenPound  
Karaduman:
it's ingenious notice))) but seriously, I believe market is really predictable
tell that to eugene fama & noble prize committee
yenPound
290
yenPound  
AngelGirl:
I am curious on what justification do any of us have to have accepted the assumption of price predictability and market inefficiency?
check out some of market hypothesis  like EMH , FMH , MFMH , ... but what i really recommend is financial market course @yale by robert shiller (which is available on youtube)
vrashtekar
140
vrashtekar  
 I think "Astrologers can never became Kings". Prediction may be correct some times but ability to take advantage of current market movements or stay away safe from it is more important to save or grow your account. 
John Seekers
793
John Seekers  
We assess the impact of spam that touts stocks upon the trading activity of those stocks and sketch how profitable such spamming might be for spammers and how harmful it is to those who heed advice in stock-touting e-mails. We find convincing evidence that stock prices are being manipulated through spam. We suggest that the effectiveness of spammed stock touting calls into question prevailing models of securities regulation that rely principally on the proper labeling of information and disclosure of conflicts of interest as means of protecting consumers, and we propose several regulatory and industry interventions.

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.
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