Motivation, workout and performance - a model for amatorial sports

The previous literature has not devoted enough space to “motivation for training” issues, especially for amateur sports. Generally, is possible imagine some factors which influence motivation for training in professional sports like an high remuneration, fame, etc. However is more difficult find these motivation factors it in the amatorial context, because an amatorial player already has not a substantial remuneration, has a job beyond sports, etc. The main result of this paper is that a large number of players in a team encourage each other to work hard during training session. All based on the assumption that more workout brings to better performance

Distribution-based entropy weighting clustering of skewed and heavy tailed time series

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.Peer ReviewedPostprint (published version

A Composite Index for Measuring Stock Market Inefficiency

Market inefficiency is a latent concept, and it is difficult to be measured by means of a single indicator. In this paper, following both the adaptive market hypothesis (AMH) and the fractal market hypothesis (FMH), we develop a new time-varying measure of stock market inefficiency. The proposed measure, called composite efficiency index (CEI), is estimated as the synthesis of the most common efficiency measures such as the returns' autocorrelation, liquidity, volatility, and a new measure based on the Hurst exponent, called the Hurst efficiency index (HEI). To empirically validate the indicator, we compare different European stock markets in terms of efficiency over time

Dies for pressing metal powders to form helical gears

Abstract This work concerns the realization of dies to produce helical gears by metal powder compaction. Due to the helicoidal geometry of the cylindrical gears, the punch, in addition to the axial motion, must necessarily rotate to "cross" the die. The innovative idea is to design a perfectly functioning system that can generate any helix angle (β) in the range of interest 0°-30°, using the simple contact between punch and die cavity during the rotation. First of all, the punch-die system was treated as a self-locking screw to determine the maximum s-value at which punch could be clamped inside the die during pressing. The analysis encouraged the execution of experimental tests related to a die with β = 5°, obtaining excellent results. Subsequently, FEM (Finite Element Method) analyses were performed on the static behavior of the die, subjected to the pressures exerted by powder and shrink-fitting ring, for three different β-values: 5°, 18° and 30°. The results obtained for the latter two angles were compared with those related to the die with β equal to 5°, considered valid thanks to experimentation, in order to theoretically verify the correct functioning even of dies with larger angles

A mixed-frequency approach for exchange rates predictions

Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding both alternative predictors (e.g., interest rates, price levels) and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach with an application to CAD/USD exchange rate predictions.

Prospective validation of the CLIP score: a new prognostic system for patient with cirrhosis and hepatocellular carcinoma

Prognosis of patients with cirrhosis and hepatocellular carcinoma (HCC) depends on both residual liver function and tumor extension. The CLIP score includes Child-Pugh stage, tumor morphology and extension, serum alfa-fetoprotein (AFP) levels, and portal vein thrombosis. We externally validated the CLIP score and compared its discriminatory ability and predictive power with that of the Okuda staging system in 196 patients with cirrhosis and HCC prospectively enrolled in a randomized trial. No significant associations were found between the CLIP score and the age, sex, and pattern of viral infection. There was a strong correlation between the CLIP score and the Okuda stage, As of June 1999, 150 patients (76.5%) had died. Median survival time was 11 months, overall, and it was 36, 22, 9, 7, and 3 months for CLIP categories 0, 1, 2, 3, and 4 to 6, respectively. In multivariate analysis, the CLIP score had additional explanatory power above that of the Okuda stage. This was true for both patients treated with locoregional therapy or not. A quantitative estimation of 2-year survival predictive power showed that the CLIP score explained 37% of survival variability, compared with 21% explained by Okuda stage. In conclusion, the CLIP score, compared with the Okuda staging system, gives more accurate prognostic information, is statistically more efficient, and has a greater survival predictive power. It could be useful in treatment planning by improving baseline prognostic evaluation of patients with RCC, and could be used in prospective therapeutic trials as a stratification variable, reducing the variability of results owing to patient selection

Shrinkage estimation with reinforcement learning of large variance matrices for portfolio selection

A large amount of assets characterizes high-dimensional portfolio selection problems compared to temporal observation. In such a high-dimensional framework, the asset allocation is unfeasible because the covariance matrix obtained with the usual sample estimators cannot be inverted. This paper proposes a new shrinkage estimator based on reinforcement learning for large covariance matrices that is optimal in the context of portfolio selection. The resulting estimator is entirely data-driven since the optimal shrinkage intensity is given by optimizing neural network weights. This paper presents two different architectures: a standard fully connected network for a classical Policy Gradient Agent (PGA) and a Gated Recurrent Unit for a Recurrent Policy Gradient Agent (RPGA). To show the validity of the proposal, an application to asset allocation with Industry portfolios is provided. The results indicate that the RPGA-based approach in shrinkage estimation provides the best performance in out-of-sample comparison

Essays in statistical methods for asset allocation

The present work, divided into three main chapters, discusses the development and the application of novel statistical techniques for portfolio selection problems. The first chapter is devoted to the estimation theory, and a new estimator for the precision matrix, called precision shrinkage, is developed to reduce the estimation error. The analysis provided in the chapter show that the use of precision shrinkage lead to the construction of more desirable portfolios in terms of return/risk trade-off with respect to well established alternatives. The second chapter studies the ability of forecasting techniques in constructing more attractive portfolios than strategies based on static estimation. Classical model-based econometric methods are compared with data-driven machine learning ones. We find that, for both low and large dimensions, the use of forecasts improves the out-of-sample portfolio performances if model-based approaches are employed. The last chapter discusses the usefulness of clustering in portfolio selection. Clustering can be used to reduce the asset allocation dimensionality. Several algorithms are compared in terms of out-of-sample profitability. As a main result, we show that clustering-based portfolios dominate the classical approaches