Market Predictability - page 6

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seekers
3960
seekers  

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.
seekers
3960
seekers  

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.
seekers
3960
seekers  

market_depth_and_order_size.pdf

In this paper we measure market depth by investigating the relation between net order flow and price changes. Two aspects are our main focus. Is the relation linear? Is the relation different for positive and negative net order flow? Answers to these questions are important for the design of market liquidity studies and for optimal trading. We use intraday data on German index futures. Our analysis based on a neural network model provides us with two main results. First, the relation between net order flow and price changes is strongly non-linear. Large orders lead to relatively small price changes whereas small orders lead to relatively large price changes. We provide an example which shows that the optimal trading strategy of informed investors depends crucially on whether the price impact is linear or not. Second, we find that buyer initiated trades lead to a smaller price change than seller initiated trades of the same size. This finding contradicts the assumption of a symmetric price impact of buy and seller orders which is commonly used in theoretical models. Overall, the results of our paper suggest that the assumption of a linear and symmetric impact of orders on prices is highly questionable. Thus, market depth cannot be described sufficiently by a single number. Therefore, empirical studies comparing liquidity of markets should be based on the whole price function instead of a simple ratio. A promising avenue of further theoretical research might be to allow the priceimpact per unit to depend on the trade volume. This should lead to quite different trading strategies as in traditional models.
John Seekers
793
John Seekers
793
John Seekers  

We document using the ZEW panel of German stock market forecasters that weak forecasters tend to be overconfident in the sense that they provide extreme forecasts and their confidence intervals are less likely to contain eventual realizations. Moderate filters based on forecast accuracy over short rolling windows are somewhat successful in improving predictability. While poor performance can be due to various factors, a filter based on a prior tendency to provide extreme forecasts also improves predictability.

techmac
2973
techmac  

Interesting

Anybody using NN in forex?

mntiwana Tiwana
4450
mntiwana Tiwana  

Hi Seekers

thanks for to all of your efforts,do you think this is also a kind of logical predictability,have a look at under given site.

"http://www.forexearlywarning.com/forex-lessons/parallel-and-inverse-analysis"

Hi techmac

plz review the above mentioned site,i think it is more close to real forecasting.

regards

notice ... it is not and never a kind of ad but i think it give traders a genuine reason why and which pair to trade.

Ievgen Ryzhov
141
Ievgen Ryzhov  

Hi

Small contribution from my side - I found this book very interesting - http://www.amazon.com/Why-Stock-Markets-Crash-Financial/dp/0691118507

yenPound
290
yenPound  
conclusion of this paper : "if" there's a holy grail it can't guarantee a profit at all
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John Seekers
793
John Seekers  

Trading Systems

Forecasting interest rates is of great concern for financial researchers, economists and players in the fixed income markets. The purpose of this study is to develop an appropriate model for forecasting the short-term interest rates i.e., commercial paper rate, implicit yield on 91 day treasury bill, overnight MIBOR rate and call money rate. The short-term interest rates are forecasted using univariate models, Random Walk, ARIMA, ARMA-GARCH and ARMA-EGARCH and the appropriate model for forecasting is determined considering six-year period from 1999. The results show that interest rates time series have volatility clustering effect and hence GARCH based models are more appropriate to forecast than the other models. It is found that for commercial paper rate ARIMA-EGARCH model is most appropriate model, while for implicit yield 91 day Treasury bill, overnight MIBOR rate and call money rate, ARIMA-GARCH model is the most appropriate model for forecasting.

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