State-dependent Asset Allocation Using Neural Networks

Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods

A study on the effect of emotional intelligence on retail investors’ behavior

Investment decisions are normally accomplished based on fundamental or technical methods. However, there are many cases where investors make their investment decisions based on their emotions. This study investigates the effects of various factors including biases representation, mental accounting and risk aversion when an investment decision is executed. In other words, the study examines the effects of emotional intelligence components on retail investors’ investment strategies on Tehran Stock Exchange (TSE). The proposed study selects a sample of 270 investors who had some experiences on TSE randomly and using a questionnaire based survey detected that there was a positive and meaningful relationship between emotional intelligence and investment decisions

Investigating the effective factors on management internal controls applying

Information technology plays an important role on increasing internal control in many organizations. In this paper, we present an empirical study to measure the impact of information technology, hiring high quality skilled management team, using high quality standards and increasing employees' awareness on managing internal control. The survey uses a questionnaire based on Likert scale and distributes among the people who work in either administration or financial sectors of governmental agencies in province of Zanjan, Iran. The results of the study indicate that the implementation of information technology positively influences management team to control their system, more effectively, using more skilled and specialized managers positively influences management internal control, an organization with suitable standard positively influences management internal control and increasing employees' awareness positively influences management internal control

Firm Size and Audit Regulation and Fraud Detection: Empirical Evidence from Iran

An auditor has the responsibility for the prevention, detection and reporting of fraud, other illegal acts and errors is one of the most controversial issues in auditing, and has been one of the most frequently debated areas amongst auditors, politicians, media, regulators and the public (Gay et al 1997). Prior research has documented a positive association between audit quality and auditor size. While some studies have used audit fee as a surrogate for audit quality, other studies have employed more direct measures, such as the outcomes of quality control reviews. Those latter studies, however, used samples that suffer from severe geographic or client type restrictions. Moreover, most studies of the quality-size relationship have focused on relatively large CPA firms. In recent years there has been considerable debate about the nature of audit practice (Salehi, 2007). Auditors also have responsibility regarding accuracy and precise of statements prepared by managers

Density of nth-Power Frees

In this note we are going to analyze the density of nth-power free integers

State-dependent asset allocation using neural networks

Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods

Attitude Control Optimization of a Virtual Telescope for X-ray Observations

In this paper, a novel approach is investigated for the attitude control of two satellites acting as a virtual telescope. The Virtual Telescope for X-ray Observations (VTXO) is a mission exploiting two 6U-CubeSats operating in precision formation. The goal of the VTXO project is to develop a space-based, X-ray imaging telescope with high angular resolution precision. In this scheme, one CubeSat carries a diffractive lens and the other one carries an imaging device to support a focal length of 100 m. In this mission, the attitude control algorithms are required to keep the two spacecrafts in alignment with the Crab Nebula observations. To meet this goal, the attitude measurements from the gyros and the star trackers are used in an extended Kalman filter, for a robust hybrid controller. Due to limited energy and the requirement of high accuracy, the energy and accuracy of attitude control is optimized for this mission