International Journal of Financial Engineering

The present value of interest in the amortization schedule or the sinking fund schedule is derived in the theorem. Even though that people prefer the level payments with smaller total amount of interest, the sum of the present value of interest and the present value of principal remains unchanged in all payment methods. The sum is just the loan amount. Rather than the traditional spreadsheet, the free MIT App Inventor is applied to create the amortization schedule and the sinking fund schedule in the mobile computing environment.

The increasingly massive use of advanced Machine Learning methodologies in the financial field sector has led credit institutions to quickly move to new FinTech technologies. This paper deals with how a battery of Artificial Neural Networks (ANN), dedicated to the automatic recognition of financial patterns of potential interest to traders, can be designed and validated. The battery of neural networks that have been designed is composed of a shallow ANN, a deep ANN with ReLu, a deep ANN with Dropout and a convolutional network (ConvNet). Depending on the type of classification problem, the ANN battery dynamically recognizes the best classifier and makes use of it for pattern recognition. The first part of the paper describes how these technologies work, the second one performs a validation of the code and the third one suggests a technical analysis application on financial time series.

This study analyzes the impact of various factors like heuristic, risk aversion, financial tools and techniques, firm’s corporate governance, and day-to-day experience on the investor’s decision-making. The sample consisted of 701 individual investors trading in the Pakistan Stock Exchange. The Ordinary Least Square (OLS) is used for the estimation of research models. The findings revealed that heuristics, risk aversion, financial tools and techniques have a significant positive effect on the investment decisions of investors. The day-to-day experience and corporate governance (CG) play an important role in investment decision-making of the financial sector in Pakistan. This study will contribute to creating awareness in a diversity of investors for investing in the equity market and increases the investors’ confidence.

The accurate forecasting of freight rate index is one of the most important issues in shipping market. The continuous and jump-diffusion stochastic differential equations are used for modeling and forecasting of Baltic exchange Dirty Tanker Index (BDTI). Actual observations and simulated data are applied to estimate the best stochastic model. The comparison of forecasting between SDE methods and the ARIMA time series models show that SDE models have better accuracy than the time series techniques.

In this paper, we study the risk-neutral investor’s equilibrium equity premium in a semi-martingale market with arbitrary, normal, binomial and gamma jumps. We simulate graphs for discrete distribution of jump amplitudes in order to study the parameter effect. The equity premium for this investor remains the same regardless of $\alpha$ and $\beta$ variations in the linear utility function. In fact, there is no optimal consumption for $\beta =1$. For normal jumps, our results are consistent with the risk-averse investor’s power utility effect on the equity premium. However, the binomial and gamma amplitudes show significant variations between risk neutrality and risk aversion.

The main objective of this study is to examine the behavior of capital buffer (whether procyclicality or countercyclical) for Pakistani banks after implementation of the BASEL-II and BASEL-III accord. The sample in this study consists of 34 commercial banks of Pakistan during the period from 2006 to 2015. The impact of business cycle on Capital Buffer has been obtained by using the two-step system Generalized Method of Moment estimation technique. The empirical findings suggest that the behavior of the banks found to be procyclicality in whole sample period while it turns to be counter-cyclical after implementation of Basel-III accord.

Forecasting assets’ prices is the aim of each trader, although the trading approaches employed may vary a lot. The development of machine learning techniques has brought the opportunity to design mechanic trading systems based on dynamic artificial neural networks. The aim of this paper is to combine traditional technical indicators [such as exponential weighted moving average (EWMA), percentage volume oscillator (PVO) and stochastic indicator — %K and %D] with the nonlinear autoregressive networks (NAR and NARX). The first part of the paper describes how neural networks designed for forecasting time series work, the second one performs a deeper validation of the code and the third one combines the dynamic networks with traditional technical indicators in order to generate reliable mechanic signals. The article ends with a back testing of the trading system performed on Dow Jones Industrial Average and on Nasdaq Composite Indexes.

In this paper, we discuss the dynamics of credit default risk for bilateral collateralized credit valuation adjusted (BCCVA) for counterparty credit risk with two positive collateral accounts by assuming default times for both investor and counterparty satisfying the density hypothesis, and the contagion risk between them being reflected by the density process. We first split the price process into three key parts, and then describe the dynamics of each part by using backward stochastic differential equations (BSDEs). As applications, we introduce the “double” Cox model, in which the BSDEs have their specific forms and thus results in this paper generalize and improve corresponding theoretic results in the existing literature. We also would like to point out that, in this paper, we do not pay attention to explore how the introduction of default contagion impacts with true market data for the BCCVA predicted by for a model without contagion versus a model with contagion, but will plan to conduct the study on the impact for BCCVA with or without contagion in a separate research project.

We develop a unified hybrid valuation framework for computing option values under stochastic volatility (SV) models with a jump component. The proposed method originates from the tree method and regime switching approximation. Conditions on the choices of key parameters for the tree design are provided to guarantee the positivity of branch probabilities. We also prove the weak convergence of the proposed method to the true stochastic models. Numerical results are provided to illustrate the effectiveness of the proposed method.

The central parity of the renminbi is determined by the closing rate on the previous day according to the central parity formation mechanism following the August 2015 reform. This paper develops a simple model to study how this mechanism affects the currency’s exchange rate dynamics. The central parity is shown to be under monotonic decay or growth conditional on its rate relative to the expected exchange rate determined by a generic stochastic process. Such dynamics requires the central bank to engage in vigorous intra-marginal intervention to halt currency depreciation (or appreciation). Such upward (or downward) bias will disappear if a randomizing factor subject to market conditions is incorporated into the mechanism. The analysis is consistent with the observations in the renminbi foreign exchange market, and the theories and results in the previous studies on target zones.

This paper proposes advanced credit risk assessment and lending operations using purchase order information from borrower firms. Purchase order information from a borrower firm is useful for financial institutions to evaluate the actual business conditions of the firm. This paper shows the application of purchase order information to lending operations and credit risk assessment, and reveals its effectiveness. First, we propose a “purchase order based” credit risk model for real-time credit risk monitoring of firms. Financial institutions can monitor the actual business conditions of borrower firms by evaluating the firm’s asset value using purchase order information. A combination of traditional firm monitoring using financial statements and high-frequency monitoring using purchase order information enables financial institutions to assess the business conditions of borrower firms more precisely and efficiently. Then, with high-frequency data, financial institutions can give borrower firms appropriate support if necessary on a timely basis. Second, we illustrate purchase order financing, which is the lending method backed by purchase order information from borrowers. With purchase order financing, firms that consistently receive purchase orders from credit-worthy firms can borrow money under more favorable lending terms than the usual lending terms based on the financial statements of the borrower firm.