Market Predictability - page 11

[John Seekers]

While a great number of predictive variables for stock returns have been suggested, their prediction power is unstable. We propose a Least Absolute Shrinkage and Selection Operator (LASSO) estimator of a predictive regression in which stock returns are conditioned on a large set of lagged covariates, some of which are highly persistent and potentially cointegrated. We establish the asymptotic properties of the proposed LASSO estimator and validate our theoretical findings using simulation studies. The application of this proposed LASSO approach to forecasting stock returns suggests that cointegrating relationships among the persistent predictors leads to a significant improvement in the prediction of stock returns over various competing models in the mean squared error sense.

[John Seekers]

A Markov regime switching model for exchange rate fluctuations, with time-varying transition probabilities, is used in constructing a monthly model for predicting currency crises in Southeast Asia. The approach is designed to avoid the estimation inconsistency that might arise from misclassification errors in the construction of crisis dummy variables which other approaches (such as probit/logit and signaling) require. Our methodology also addresses the serial correlations and sudden behavior inherent in crisis occurrence, identifies a set of reliable and observable indicators of impending crisis difficulties, delivers forecast probabilities of future crises over multi-period forecasting horizons, and offers an empirical framework for analyzing contagion effects of a crisis. Our empirical results indicate that the Markov switching model is moderately successful at predicting crisis episodes, but also points to future research in various directions. Most early warning systems for currency crises have used either probit or signaling. Several issues can be raised regarding these techniques: the need for a priori dating of crisis occurrence, the use of arbitrary thresholds, inadequate modeling of the dynamics in the system, among others. We present an alternative framework, based on a Markov-switching model of exchange rate fluctuations with time-varying transition probabilities, which addresses these concerns.

[krelian99]

seekers_: We introduce Kinetic Component Analysis (KCA), a state-space application that extracts the signal from a series of noisy measurements by applying a Kalman Filter on a Taylor expansion of a stochastic process. We show that KCA presents several advantages compared to other popular noise-reduction methods such as Fast Fourier Transform (FFT) or Locally Weighted Scatterplot Smoothing (LOWESS): First, KCA provides band estimates in addition to point estimates. Second, KCA further decomposes the signal in terms of three hidden components, which can be intuitively associated with position, velocity and acceleration. Third, KCA is more robust in forecasting applications. Fourth, KCA is a forward-looking state-space approach, resilient to structural changes. We believe that this type of decomposition is particularly useful in the analysis of trend-following, momentum and mean-reversion of financial prices.

An instrument exhibits financial inertia when its price acceleration is not significantly greater than zero for long periods of time. Our empirical analysis of 19 of the most liquid futures worldwide confirms the presence of strong inertia across all asset classes. We also argue that KCA can be useful to market makers, liquidity providers and faders for the calculation of their trading ranges.

Thanks a lot, Paperspreaderino Seekerino.

[John Seekers]

The purpose of this paper is to examine the problem faced by the portfolio manager attempting to optimally incorporate forecasts of future market returns into his portfolio. Given the solution to this problem we then shall focus our attention on the problem involved in measuring a portfolio manager's ability when he is explicitly engaged in forecasting the prices on individual securities (i.e., security analysis) and in forecasting the future course of market prices (i.e., timing activities). We shall consider these problems here in the context of the Sharpe-Lintner mean-variance general equilibrium model of the pricing of capital assets, and in the context of the expanded two factor version of the Sharpe model suggested by Black, Jensen and Scholes (1972) In addition we shall concentrate our attention here on an investigation of just what can and cannot be said about portfolio performance solely on the basis of data on the time series of portfolio and market returns.

In section 2 we outline the foundations of the analysis and its relationship to the general equilibrium structure of security prices given by the Sharpe-Lintner model. In section 3 we briefly summarize the measure of security selection ability suggested by Jensen (1968). Section 4 contains a solution to the problem of the optimal incorporation of market forecasts into portfolio policy and provides the structure for the analysis in section 5 of the measurement problems introduced into the evaluation of portfolio performance by market forecasting activities by the portfolio manager. Section 6 presents the complete development of the model within the two factor equilibrium model of the pricing of capital assets suggested by Black (1970) and Black, Jensen and Scholes (1972). Section 7 contains a brief summary of the conclusions of the analysis.

[John Seekers]

Data on individual trades in prediction markets relating to the 2008 and 2012 US Presidential elections reveal that traders vary enormously in their behavior. This contrasts with the standard prediction-market models, which assume relatively homogeneous participants who differ only in their beliefs and wealth. We show that risk-lovers have particularly strong distortionary effects on market outcomes even when beliefs are symmetrically distributed around the truth. Simulations of a model which allows traders to have different motives and tastes for risk indicate that including such traders produce the market outcomes we observe, such as herding, persistent contrariness, a skewed profits’ distribution and favorite-long-shot bias. The attraction of such markets to risk-lovers means that caution must be exercised when using prediction-market prices for forecasting.

[John Seekers]

The present study is an attempt to evaluate the predictability of the foreign exchange volatility in thirteen countries. The data covers the period of 2005-2009. To effectively forecast the volatility in the exchange rates, a GARCH model is used. The study compares the results between crisis period and a set of normal periods. The empirical results reveal that almost all countries except Thailand witnessed non-existence of volatility shocks at least once in a three year pre-crisis period but all the sample countries had volatility shocks in the crisis period of 2008-09. This apparently indicates that forecasting can be made at least for the next day given the high degree of volatility in the crisis period. The paper also reveals that exchange rates tend to have persistent conditional heteroskedasticity, and hence, could be predicted with one lag term.

[John Seekers]

Thuesday 06 October 2016 at 07pm (US hour) the GBP and its ForEx crosses, shows a massive SellOff, with moderate volume level and a violent evolution in 10 minutes about. In fact there are only two 5min bars on the chart and the hourly, daily and weekly frames shows the same long-tail candle structure. Some traders and analyst pointed out that the mini BlackSwan of the GBP is a scandalous phenomenon in the normal dynamics of the financial markets of the currencies. The aim of this post is to search elements supporting or not this view. Elements used ar very similar to previous post regarding BitCoin [#01] large SellOff in 2014, and CHF [#02] historical BlackSwan of 2015. ☒ ITA Abstract. In questo post si tenta di chiarire i violenti avvenimenti ribassisti della Sterlina subiti il 6 ottobre 2016. Tutti i dati raccolti (struttura grafica locale post-2009; grafico di lungo termine; CoT; indicatore di instabilità politica UK; evento BrExit) concordano in una evoluzione ribassista della Sterlina in seguito alla rottura nel 2016 (BrExit) del livello critico 140. Il target naturale sarebbe area 100 in caso di non recupero della base a 140. Quindi i dati grafici, politici ed economici, mostrano che la rapida evoluzione di prezzo della Sterlina durante il 6 ottobre 2016, risulta solo una naturale evoluzione-conseguenza e non un evento scandaloso, e inoltre non costituisce nemmeno un evento raro e isolato su base mensile-settimanale. La fase post BrExit del prezzo della sterlina (nuovi minimi al di sotto di 140; assenza di recupero) era un chiaro segno di ulteriore debolezza in arrivo. Del resto i principali attori CoT erano correttamente posizionati fin dal 2014-2015, non come accadde sul CHF BlackSwan [#02]. ☒ Graphical Elements. The very long term structure of GBP index shows a large bearish pattern (large red rectangle) with a first hard phase in 1981-1984 (''A''), a very impulsive phase. A second phase (B1-B2-B3) as a large side action, but well below (in average) vs. mid-level of the previous fast leg ''A'' (175-180). B3 top demarcates the restart of violent bearish action of ''A'' with a very similar behaviour until to 2009 lows. The next phase is local blue side rectangle, pre-BrExit (see below). Bearish of 2009 and 2016 broken down two important ascending rectangles (blue trend lines), visibly breaking the multiannual attempt to recovery the violent declines in ''80 years. The level 140 about, for GBP Index future, is a very critical value on very long term frame, according to some previous key lows (yellow circles). Only during 1984-1985, GBP was below this critical level, with a very fast and violent behaviour. From the 2009 bottom (on 140 level about), there is a local side structure (in blue) under bearish attack in 2016 (BrExit event), and with possible bearish target at 100 (blue columns). The BrExit event cause a monstre SellOff monthly candle, but not the largest (see monthly bars of 1981, 1992, 2008), with bearish activation of the side blue rectangle. Infact the level of 140 about was broken down during June 2016 (BrExit), without a subsequet recovery, but with new lows below 140. The main bearish actions from 2008, shows fast increase and positive spread between Commercial players vs. Large Traders on CoT curves (see yellow lines). The top of this spread is in 2016. The economic policy uncertainty index of UK, shows historical top values also pre-BrExit (2016 first quarter). In the phase BrExit this index obtain an unprecedented top of policy instability evaluations for UK. ☒ Attached Charts http://bit.ly/2dJGJAI http://bit.ly/2e3pZDc http://bit.ly/2dqqLJC ☒ Notes. All these data (local; long term; CoT; policy instability; BrExit event) agree in a continuation-bear evolution of GBP until area 100. This scenario is stopped if the GBP prices go above the base of the blue rectangle 2009-2016; It is canceled if the GBP prices go above the mid-line of the blue rectangle itself. Graphical, political and economic data shows that the fast price evolution of GBP of Thuesday 06 October 2016 is a natural evolution and not a scandalous event; moreover, according to GBP long term chart, it is not rare and isolate on monthly-weekly base. The post BrExit phase of GBP (new lows below 140, recovery-less) was a very clear sign of further weakness. The CoT main players are correctly positioned from 2014-2015, not as in CHF BlackSwan [#02]. ☒ Chart sources. ❖ FinViz; St.LouisFED; TimingCharts. Bibliography. Salvatore Salvi Vicidomini, 2014 [#01] - Financial Markets Observatory Lab. Some notes/charts about the intraday giant spikes of BitCoin prices. - ForexFactory thread - https://www.researchgate.net/publication/260159249 Salvatore Salvi Vicidomini, 2015 [#02] - Financial Markets Observatory Lab. Notes and charts about the ForEx BlackSwan of Swiss Franc (CHF), powered by Swiss National Bank in January 15, 2015, based on a qualitative analysis of CoT curves. - ForexFactory thread - http://www.researchgate.net/publication/271137015

[John Seekers]

Financial planning can be define as a group of plans to get necessary financing resources and use it. That's mean identify the financial needs in specific period. And we most know that a financial planning one of most important elements of organization financing strategy, because it convert this strategy to practical steps we can apply it. Financial planning depends on financial forecasting which basically depend on organizations sales, maybe one question come on our minds, why we chose the sales to financial forecasting?. Because it has a relation with organizations assets and liabilities.to descript the relation between sales and asset we can said that organization increase assets when it tries to increase the sales. In other words when organization starts product, it needs to increase the material inventory which is necessary for production and to increase the sales. In addition, the organization sales will not be necessary in cash, it can be on credit, that refers to increasing in organizations credit balance. From other side the organization need to increase the financing resources to accomplish the increasing sales goal, which shows to us the relation between sales and liabilities. Financial forecasting by using the sales percentage method; 1. Identify the budget accounts which is changeable with the sales. 2. Find the budget accounts percentage by divide it on the recently sales (S). 3. Collecting the asset and liabilities percentage (A/s & L/s). 4. Find the E.F.R (External funds required) by using incoming formula; E.F.R = (S 2-S 1) (A/S – L/S)-(MBS 2). E.F.R: External funds required. S 1: recently sales. S 2 : forecasted sales. A/S : total asset percentage that is changeable with the sales. L/S: total liabilities percentage that changeable with the sales. M: marginal profit percentage. B: returned earning percentage. 5. Now regroup the budget accounts percentage in new budget (forecasted budget). 6. Multiply each account percentage by forecasted sales sum. 7. Regroup the budget account that isn't changeable with the sales in forecasted budget. 8. We most edit returned earning sum, by add the (MBS 2) sum to the returned earning balance. 9. Add E.F.R sum with liabilities in forecasted budget to balance both credit and debt side.

[John Seekers]

This paper propose that the combination of smoothing approach taking into account the entropic information provided by Renyi method, has an acceptable performance in term of forecasting errors. The methodology of the proposed scheme is examined through benchmark chaotic time series, such as Mackay Glass, Lorenz, Henon maps, the Lynx and rainfall from Santa Francisca series, with addition of white noise by using neural networks-based energy associated (EAS) predictor filter modified by Renyi entropy of the series. In particular, when the time series is short or long, the underlying dynamical system is nonlinear and temporal dependencies span long time intervals, in which this are also called long memory process. In such cases, the inherent nonlinearity of neural networks models and a higher robustness to noise seem to partially explain their better prediction performance when entropic information is extracted from the series. Then, to demonstrate that permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series, computational results are evaluated against several non-linear ANN predictors proposed before to show the predictability of noisy rainfall and chaotic time series reported in the literature.

[John Seekers]

Under Black-Scholes (BS) assumptions, empirical volatility and risk neutral volatility are given by a single parameter, which captures all aspects of risk. Inverting the model to extract implied volatility from an option's market price gives the market's forecast of future empirical volatility. But real world returns are not lognormal, volatility is stochastic, and arbitrage is limited, so option prices embed both the market's estimate of the true returns distribution and also investors' risk attitudes, including possibly different preferences over specific volatility-related aspects of the returns process, such as tail risk. Using options with a dense set strikes, we can obtain the entire risk neutral density (RND) which reflects all of these influences without requiring restrictive assumptions from a pricing model.

We compute daily RNDs for the S&P 500 index over 15 years and find that risk neutral volatility is strongly influenced both by investors' projections of future volatility under the empirical distribution and also by the risk neutralization process. Several significant variables are connected in different ways to realized volatility, such as the daily trading range and tail risk; others are meant to reflect risk attitudes, such as the level of investor confidence and the size of recent volatility forecast errors. As a forecast of future volatility, RND volatility fully impounds the information in historical volatility, but not a more sophisticated GARCH forecast, and its forecasting power seems greatest in the range of 1 to 2 weeks ahead.