Confronto tra sistemi trend following e mean reversion

Si descrivono strategie di trading trend following e strategie mean reversion applicate a vari strumenti finanziari

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Mean reversion versus random walk in G7 stock prices evidence from multiple trend break unit root tests

This paper provides evidence on the random walk hypothesis in G7 stock price indices using unit root tests which allow for one and two structural breaks in the trend. Of the seven countries we find, at best, evidence of mean reversion in the stock price index of Japan. Thus, overall, our results support the random walk hypothesis. We also consider the implications of the identified structural breaks for movement in stock prices over time. Our main conclusion from this exercise is that the second break in stock prices has had a detrimental effect on movements in stock prices in the G7 countries.

Short-Horizon Asymmetric Mean-Reversion and Overreactions: Evidence from the Nordic Stock Markets

This paper examines the asymmetric behavior of conditional mean and variance. Short-horizon mean-reversion behavior in mean is modeled with an asymmetric nonlinear autoregressive model, and the variance is modeled with an Exponential GARCH in Mean model. The results of the empirical investigation of the Nordic stock markets indicates that negative returns revert faster to positive returns when positive returns generally persist longer. Asymmetry in both mean and variance can be seen on all included markets and are fairly similar. Volatility rises following negative returns more than following positive returns which is an indication of overreactions. Negative returns lead to increased variance and positive returns leads even to decreased variance.

Carry and trend-following strategies

Recent research has shown that carry and trend strategies when combined lead to significant risk-adjusted returns that can be very attractive to investors, at a low cost with small and positive skewness. This study proposes to combine both carry and trend-following, considering a data set of ten years (09/2005-09/2015), within a portfolio composed by three major asset classes: currencies, commodities and equity indices. Following a futures-based methodology, the obtained results show that, indeed, the strategy results inevitably in higher returns and greater sharpe ratios for every asset class in study. This outcome results from the fact that trend proved to provide a significant hedge to the downside risk that carry is exposed to.

Mean reversion in stock prices: new evidence from panel unit root tests

Purpose – There are several studies that investigate evidence for mean reversion in stock prices. However, there is no consensus as to whether stock prices are mean reverting or random walk (unit root) processes. The goal of this paper is to re-examine mean reversion in stock prices.
Design/methodology/approach – The authors use five different panel unit root tests, namely the Im, Pesaran and Shin t-bar test statistic, the Levin and Lin test, the Im, Lee, and Tieslau Lagrangian multiplier test statistic, the seemingly unrelated regression test, and the multivariate augmented Dickey Fuller test advocated by Taylor and Sarno.
Findings – The main finding is that there is no mean reversion of stock prices, consistent with the efficient market hypothesis.
Research limitations/implications – One issue not considered by this study is the role of structural breaks. It may be the case that the efficient market hypothesis is contingent on structural breaks in stock prices. Future studies should model structural breaks.
Practical implications – The findings have implications for econometric modelling, in particular forecasting.
Originality/value – This paper adds to the scarce literature on the mean reverting property of stock prices based on panel data; thus, it should be useful for researchers.

Trend following performance with size and liquidity: evidence from US, Brazil and Portugal

Neste trabalho, eu analiso a eficiência de se aplicar estratégias que identificam tendências em mercados de capitais, em três países diferentes, usando um conjunto de variáveis macroeconómicas. Em cada país, a estratégia é testada contra os índices de grande capitalização, pequena capitalização e o índice principal. Eu concluo que, ao combinar os sinais diários obtidos pela estratégia, é possível alcançar retornos ajustados ao risco superiores e reduzir as perdas possíveis do portfólio. No geral, enfatizo os benefícios de usar estratégias que exploram tendências para investidores avessos ao risco, obtendo retornos característicos de capitais próprios com a volatilidade característica de obrigações.

Mean reversion in the short horizon returns of UK portfolios

This paper will show that short horizon stock returns for UK portfolios are more predictable than suggested by sample autocorrelation co-efficients. Four capitalisation based portfolios are constructed for the period 1976–1991. It is shown that the first order autocorrelation coefficient of monthly returns can explain no more than 10% of the variation in monthly portfolio returns. Monthly autocorrelation coefficients assume that each weekly return of the previous month contains the same amount of information. However, this will not be the case if short horizon returns contain predictable components which dissipate rapidly. In this case, the return of the most recent week would say a lot more about the future monthly portfolio return than other weeks. This suggests that when predicting future monthly portfolio returns more weight should be given to the most recent weeks of the previous month, because, the most recent weekly returns provide the most information about the subsequent months' performance. We construct a model which exploits the mean reverting characteristics of monthly portfolio returns. Using this model we forecast future monthly portfolio returns. When compared to forecasts that utilise the autocorrelation statistic the model which exploits the mean reverting characteristics of monthlyportfolio returns can forecast future returns better than the autocorrelation statistic, both in and out of sample.

Are OECD stock prices characterized by a random walk? Evidence from sequential trend break and panel data models

This paper examines whether stock prices for a sample of 22 OECD countries can be best represented as mean reversion or random walk processes. A sequential trend break test proposed by Zivot and Andrews is implemented, which has the advantage that it can take account of a structural break in the series, as well as panel data unit root tests proposed by Im et al., which exploits the extra power in the panel properties of the data. Results provide strong support for the random walk hypothesis.

Monte Carlo Valuation of natural gas investments

This paper deals with the valuation of energy assets related to natural gas. In particular, we evaluate a baseload Natural Gas Combined Cycle (NGCC) power plant and an ancillary instalation, namely a Liquefied Natural Gas (LNG) facility, in a realistic setting; specifically, these investments enjoy a long useful life but require some non-negligible time to build. Then we focus on the valuation of several investment options again in a realistic setting. These include the option to invest in the power plant when there is uncertainty concerning the initial outlay, or the option's time to maturity, or the cost of CO2 emission permits, or when there is a chance to double the plant size in the future. Our model comprises three sources of risk. We consider uncertain gas prices with regard to both the current level and the long-run equilibrium level; the current electricity price is also uncertain. They all are assumed to show mean reversion. The two-factor model for natural gas price is calibrated using data from NYMEX NG futures contracts. Also, we calibrate the one-factor model for electricity price using data from the Spanish wholesale electricity market, respectively. Then we use the estimated parameter values alongside actual physical parameters from a case study to value natural gas plants. Finally, the calibrated parameters are also used in a Monte Carlo simulation framework to evaluate several American-type options to invest in these energy assets. We accomplish this by following the least squares MC approach.

Endurance training modulates lymphocyte function in rats with post-MI CHF

Purpose: Exercise training restores innate immune system cell function in post-myocardial infarction (post-MI) rats. However, studies of the involvement of lymphocyte (Ly) in the setting of the congestive heart failure (CHF) are few. To address this issue, we investigated the function of Ly obtained from cervical lymph nodes from post-MI CHF rats submitted to treadmill running training. Methods: Twenty-five male Wistar rats were randomly assigned to the following groups: rats submitted to ligation of the left coronary artery, which were sedentary (MI-S, N= 7, only limited activity) or trained (MI-T, N= 6, on a treadmill (0% grade at 13-20 m.m(-1)) for 60 min.d(-1), 5 d.wk(-1), for 8-10 wk); or sham-operated rats, which were sedentary (sham-S, N = 6) or trained (sham-T, N = 6). The incorporation of [2-C-14]-thymidine by Ly cultivated in the presence of concanavalin A (Con A) and lipopolysaccharide (LPS), cytokine production by Ly cultivated in the presence of phytohemagglutinin (PHA), and plasma concentration of glutamine were assessed in all groups, 48 h after the last exercise session. Results: Proliferative capacity was increased, following incubation with Con-A in the MI groups, when compared with the sham counterparts. When incubated in the presence of PHA, MI-S produced more IL-4 (96%) than sham-S (P < 0.001). The training protocol induced a 2.2-fold increase in the production of interleukin-2 (P < 0.001) of the cells obtained from the cervical lymph nodes of MI-T, compared with MI-S. Conclusion: The moderate endurance training protocol caused an increase in IL-2 production, and a trend toward the reversion of the Th-1/Th-2 imbalance associated with IL-4 production increased in the post-MI CHF animal model.

Dois ensaios em finanças

In the first chapter, we test some stochastic volatility models using options on the S&P 500 index. First, we demonstrate the presence of a short time-scale, on the order of days, and a long time-scale, on the order of months, in the S&P 500 volatility process using the empirical structure function, or variogram. This result is consistent with findings of previous studies. The main contribution of our paper is to estimate the two time-scales in the volatility process simultaneously by using nonlinear weighted least-squares technique. To test the statistical significance of the rates of mean-reversion, we bootstrap pairs of residuals using the circular block bootstrap of Politis and Romano (1992). We choose the block-length according to the automatic procedure of Politis and White (2004). After that, we calculate a first-order correction to the Black-Scholes prices using three different first-order corrections: (i) a fast time scale correction; (ii) a slow time scale correction; and (iii) a multiscale (fast and slow) correction. To test the ability of our model to price options, we simulate options prices using five different specifications for the rates or mean-reversion. We did not find any evidence that these asymptotic models perform better, in terms of RMSE, than the Black-Scholes model. In the second chapter, we use Brazilian data to compute monthly idiosyncratic moments (expected skewness, realized skewness, and realized volatility) for equity returns and assess whether they are informative for the cross-section of future stock returns. Since there is evidence that lagged skewness alone does not adequately forecast skewness, we estimate a cross-sectional model of expected skewness that uses additional predictive variables. Then, we sort stocks each month according to their idiosyncratic moments, forming quintile portfolios. We find a negative relationship between higher idiosyncratic moments and next-month stock returns. The trading strategy that sells stocks in the top quintile of expected skewness and buys stocks in the bottom quintile generates a significant monthly return of about 120 basis points. Our results are robust across sample periods, portfolio weightings, and to Fama and French (1993)’s risk adjustment factors. Finally, we identify a return reversal of stocks with high idiosyncratic skewness. Specifically, stocks with high idiosyncratic skewness have high contemporaneous returns. That tends to reverse, resulting in negative abnormal returns in the following month.

Analysis of operational risk of banks – catastrophe modelling

Nowadays financial institutions due to regulation and internal motivations care more intensively on their risks. Besides previously dominating market and credit risk new trend is to handle operational risk systematically. Operational risk is the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. First we show the basic features of operational risk and its modelling and regulatory approaches, and after we will analyse operational risk in an own developed simulation model framework. Our approach is based on the analysis of latent risk process instead of manifest risk process, which widely popular in risk literature. In our model the latent risk process is a stochastic risk process, so called Ornstein- Uhlenbeck process, which is a mean reversion process. In the model framework we define catastrophe as breach of a critical barrier by the process. We analyse the distributions of catastrophe frequency, severity and first time to hit, not only for single process, but for dual process as well. Based on our first results we could not falsify the Poisson feature of frequency, and long tail feature of severity. Distribution of “first time to hit” requires more sophisticated analysis. At the end of paper we examine advantages of simulation based forecasting, and finally we concluding with the possible, further research directions to be done in the future.