scholarly journals Index tracking strategy based on mixed-frequency financial data

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249665
Author(s):  
Xiangyu Cui ◽  
Xuan Zhang
Keyword(s):  
Financial Data ◽  
Index Tracking ◽  
Sample Analysis ◽  
Tracking Accuracy ◽  
Mixed Frequency ◽  
Average Return ◽  
Proposed Model ◽  
Out Of Sample ◽  
Daily Risk ◽  
Tracking Strategy

To obtain market average return, investment managers need to construct index tracking portfolio to replicate target index. Currently, most literatures use financial data that has homogenous frequency when constructing the index tracking portfolio. To make up for this limitation, we propose a methodology based on mixed-frequency financial data, called FACTOR-MIDAS-POET model. The proposed model can utilize the intraday return data, daily risk factors data and monthly or quarterly macro economy data, simultaneously. Meanwhile, the out-of-sample analysis demonstrates that our model can improve the tracking accuracy.

Simple Multivariate Conditional Covariance Dynamics Using Hyperbolically Weighted Moving Averages

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hiroyuki Kawakatsu
Keyword(s):  
Moving Average ◽  
Curse Of Dimensionality ◽  
Moving Averages ◽  
Conditional Covariance ◽  
Arch Models ◽  
Empirical Application ◽  
Proposed Model ◽  
Out Of Sample ◽  
Weight Model ◽  
Weighted Moving Average

AbstractThis paper considers a class of multivariate ARCH models with scalar weights. A new specification with hyperbolic weighted moving average (HWMA) is proposed as an analogue of the EWMA model. Despite the restrictive dynamics of a scalar weight model, the proposed model has a number of advantages that can deal with the curse of dimensionality. The empirical application illustrates that the (pseudo) out-of-sample multistep forecasts can be surprisingly more accurate than those from the DCC model.

scholarly journals An approach to predict Spanish mortgage market activity using Google data

2019 ◽  
Vol 8 (4) ◽  
pp. 209
Author(s):  
Marcos González-Fernández ◽  
Carmen González-Velasco
Keyword(s):  
Prediction Accuracy ◽  
Mortgage Market ◽  
Sample Analysis ◽  
Market Activity ◽  
Out Of Sample

The aim of this paper is to use Google data to predict Spanish mortgage market activity during the period from January 2004 to January 2019. Thus, we collect monthly Google data for the keyword hipoteca, the Spanish expression for mortgage, and then, we perform a regression and an out-of-sample analysis. We find evidence that the use of Google data significantly improves prediction accuracy.

scholarly journals A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment

2020 ◽  
Vol 13 (7) ◽  
pp. 155
Author(s):  
Zhenlong Jiang ◽  
Ran Ji ◽  
Kuo-Chu Chang
Keyword(s):  
Machine Learning ◽  
Risk Aversion ◽  
Financial Data ◽  
Rolling Horizon ◽  
Learning Models ◽  
Market Index ◽  
Out Of Sample ◽  
The Mean ◽  
Mean Risk ◽  
Machine Learning Models

We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by machine learning models. We employ Gini’s Mean Difference (GMD) to specify the risk of a portfolio and use a set of technical indicators generated from a market index (e.g., S&P 500 index) to feed the machine learning models to predict market movements. Using a rolling-horizon approach, we conduct a series of computational tests with real financial data to evaluate the performance of the machine learning integrated portfolio rebalance framework. The empirical results show that the XGBoost model provides the best prediction of market movement, while the proposed portfolio rebalance strategy generates portfolios with superior out-of-sample performances in terms of average returns, time-series cumulative returns, and annualized returns compared to the benchmarks.

Out‐of‐sample volatility prediction: A new mixed‐frequency approach

2019 ◽  
Vol 38 (7) ◽  
pp. 669-680 ◽  
Author(s):  
Yaojie Zhang ◽  
Feng Ma ◽  
Tianyi Wang ◽  
Li Liu
Keyword(s):  
Mixed Frequency ◽  
Out Of Sample

scholarly journals Forecasting Realized Volatility Using a Nonnegative Semiparametric Model

2019 ◽  
Vol 12 (3) ◽  
pp. 139 ◽  
Author(s):  
Anders Eriksson ◽  
Daniel P. A. Preve ◽  
Jun Yu
Keyword(s):  
Predictive Accuracy ◽  
Estimation Method ◽  
Semiparametric Model ◽  
Error Component ◽  
New Model ◽  
Finite Samples ◽  
Proposed Model ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Using Data

This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model extends a related linear nonnegative autoregressive model previously used in the volatility literature by way of a power transformation. It is semiparametric in the sense that the distributional and functional form of its error component is partially unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new method and suggest that it works reasonably well in finite samples. The out-of-sample forecasting performance of the proposed model is evaluated against a number of standard models, using data on S&P 500 monthly realized volatilities. Some commonly used loss functions are employed to evaluate the predictive accuracy of the alternative models. It is found that the new model generally generates highly competitive forecasts.

scholarly journals ANNs-Based Early Warning System for Indonesian Islamic Banks

2018 ◽  
Vol 20 (3) ◽  
pp. 325-342 ◽  
Author(s):  
Saiful Anwar ◽  
A.M Hasan Ali
Keyword(s):  
Financial Performance ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Financial Conditions ◽  
Proposed Model ◽  
Selection Of Variables ◽  
Sample Data ◽  
Out Of Sample ◽  
Macroeconomic Indicators

This research proposes a development of Early Warning System (EWS) model towards the financial performance of Islamic bank using financial ratios and macroeconomic indicators. The result of this paper is ready-to-use algorithm for the issue that needs to be solved shortly using machine learning technique which is not widely applied in Islamic banking. The research was conducted in three stages using Artificial Neural Networks (ANNs) technique: the selection of variables that significantly affect financial performance, developing an algorithm as a predictor and testing the predictor algorithm using out of sample data. Finally, the research concludes that the proposed model results in 100% accuracy for predicting Islamic bank’s financial conditions for the next two consecutive months.

scholarly journals Gestão de carteiras sob múltiplos regimes: Performance fora da amostra

2020 ◽  
Vol 18 (3) ◽  
pp. 52
Author(s):  
Marcelo Lewin ◽  
Carlos Heitor Campani
Keyword(s):  
Comparative Analysis ◽  
Approximate Analytical Solution ◽  
Portfolio Allocation ◽  
Dynamic Allocation ◽  
Allocation Strategy ◽  
Confidence Levels ◽  
Average Return ◽  
Multiple Regimes ◽  
Out Of Sample ◽  
Asset Classes

<p>We propose a dynamic allocation strategy for an investor which considers three unobservable economic regimes, which we estimate using returns on five Brazilian asset classes. The strategy is based on an approximate analytical solution of a realistic configuration of the economy. The out-of-sample performance exceeds those of every benchmark we consider in 6 out of 10 years, with a weekly average return significantly higher than any benchmark at the usual confidence levels. From 2010 to 2019, our strategy achieves an average return of 21.6% per annum against, for example, 9.8% p.a. of the CDI and 4.7% p.a. of the Ibovespa. In particular, a comparative analysis makes clear how important it is to include multiple regimes in portfolio allocation.</p>

scholarly journals Enhanced indexation via chance constraints

2020 ◽  
Author(s):  
Patrizia Beraldi ◽  
Maria Elena Bruni
Keyword(s):  
Investment Strategy ◽  
Chance Constraints ◽  
Portfolio Performance ◽  
Investment Strategies ◽  
Index Tracking ◽  
Negative Deviation ◽  
Additional Risk ◽  
Benchmark Instances ◽  
Out Of Sample ◽  
The Empirical Analysis

Abstract The enhanced index tracking (EIT) represents a popular investment strategy designed to create a portfolio of assets that outperforms a benchmark, while bearing a limited additional risk. This paper analyzes the EIT problem by the chance constraints (CC) paradigm and proposes a formulation where the return of the tracking portfolio is imposed to overcome the benchmark with a high probability value. Besides the CC-based formulation, where the eventual shortage is controlled in probabilistic terms, the paper introduces a model based on the Integrated version of the CC. Here the negative deviation of the portfolio performance from the benchmark is measured and the corresponding expected value is limited to be lower than a given threshold. Extensive computational experiments are carried out on different set of benchmark instances. Both the proposed formulations suggest investment strategies that track very closely the benchmark over the out-of-sample horizon and often achieve better performance. When compared with other existing strategies, the empirical analysis reveals that no optimization model clearly dominates the others, even though the formulation based on the traditional form of the CC seems to be very competitive.

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