Portfolio Sensitivity Model for Analyzing Credit Risk Caused by Structural and Macroeconomic Changes

This paper proposes a new model for portfolio sensitivity analysis. The model is suitable for decision support in financial institutions, specifically for portfolio planning and portfolio management. The basic advantage of the model is the ability to create simulations for credit risk predictions in cases when we virtually change portfolio structure and/or macroeconomic factors. The model takes a holistic approach to portfolio management consolidating all organizational segments in the process such as marketing, retail and risk.portfolio analysis, credit risk, weighting, scoring, data mining, sensitivity analyses, decision support, Bayesian networks, BASEL II

EX POST AND EX ANTE IMPACT ANALYSIS OF RESEARCH PROGRAMMES USING CMMI FRAMEWORK

In this paper, we propose a new approach for evaluating research projects and programs. According our approach, the improvement might be achieved by adopting a results-based and a project portfolio approach, and assuring a research and technology development (RTD) indicators documentation through a standard and comprehensive indicator description, named indicator template. The results-based approach will assure a consistent indicators structure, according to the results chains and a strong connection between ex ante and ex post impact evaluation. The project portfolio approach will assure a tight integration of the research performance indicators, especially between policies goals and program results. And, finally defining a comprehensive indicator template it will be possible to understand better the indicators, develop a detailed analysis, based on the business intelligence techniques, such as OLAP (On-Line Analytical Processing), data mining and text mining. According our knowledge the usage of this kind of techniques on RTD metadata is an innovative process. What we expect to find out is the indicators similarities and differentiations, the indicators clusters, the association between indicators, the most important input factors of indicators definition. According the results-based and project portfolio approach the discovered patterns will be evaluatedRDT indicator, RDT statistics, indicator template, data mining, text mining.

Diversification Across Mining Pools: Optimal Mining Strategies under PoW

Mining is a central operation of all proof-of-work (PoW) based cryptocurrencies. The vast majority of miners today participate in "mining pools" instead of "solo mining" in order to lower risk and achieve a more steady income. However, this rise of participation in mining pools negatively affects the decentralization levels of most cryptocurrencies. In this work, we look into mining pools from the point of view of a miner: We present an analytical model and implement a computational tool that allows miners to optimally distribute their computational power over multiple pools and PoW cryptocurrencies (i.e. build a mining portfolio), taking into account their risk aversion levels. Our tool allows miners to maximize their risk-adjusted earnings by diversifying across multiple mining pools which enhances PoW decentralization. Finally, we run an experiment in Bitcoin historical data and demonstrate that a miner diversifying over multiple pools, as instructed by our model/tool, receives a higher overall Sharpe ratio (i.e. average excess reward over its standard deviation/volatility).Comment: 13 pages, 16 figures. Presented at WEIS 201

DATA MINING BASED MODEL AGGREGATION

Applying modelling techniques for getting acquainted with customer behaviour, predicting the customers’ next step is neccessary to keep in competition, by decreasing the capital requirement (Basel II - IRB) or making the portfolio more profitable. According to the easily implementable modelling techniques, data mining solutions widespread in practice. Using these models with no conditions can lead into inconsistent future on portfolio change. Consequence of this situation, contradictory predictions and conclusions come into existence. Recognizing and conscious handling of inconsistent predictions is an important task for experts working on different scene of the knowledge based economy and society. By realizing and solving the problem of inconsistency in modelling processes, the competitive advantage can be increased and strategic decisions can be supported by consistent predictions.model aggregation, consistent future, data mining, CRM, Basel II, Research and Development/Tech Change/Emerging Technologies,

Statistical Arbitrage Mining for Display Advertising

We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2015

Vine copula modelling of dependence and portfolio optimization with application to mining and energy stock return series from the Australian market

This thesis models the dependence risk profile, investment risk and portfolio allocation features of seven 20-stock portfolios from the mining, energy, retail and manufacturing sectors of the Australian market in the context of the 2008-2009 global financial crisis (2008-2009 GFC) and pre-GFC, GFC, post-GFC and full sample period scenarios revolving around it. The mining and energy portfolios are the base of the study, while the retail and manufacturing are considered for benchmarking purposes. Pair vine copula models including canonical vines (c-vines), drawable vines (d-vines) and regular vines (r-vines) are fitted for the analysis of the portfolios’ multivariate dependence and their underlying sectors’ dependence risk dynamics. Besides, linear and nonlinear optimization methods threaded with the variance, mean absolute deviation (MAD), minimizing regret (Minimax), conditional Value-at-Risk (CVaR) and conditional Drawdown-at-Risk (CDaR) risk measures are implemented to examine the portfolios’ investment risk and optimal portfolio allocation features. The vine copula modelling of dependence aims at examining the dependence risk profile of the portfolios in specific market conditions; studying the changes of the portfolios’ dependence structure between pairs of period scenarios; and recognizing the vine copula models that best account for the portfolios’ multivariate dependence. The multiple risk measure-based portfolio optimization seeks to identify the least and most investment risky portfolios, single out the portfolio that offers the best risk-return trade-off and recognize the stocks in the portfolios that are good candidates for investment. This thesis’ main contributions stem from the “copula counting technique” and “average model convergence” perspectives proposed to handle, analyse and interpret the portfolios’ dependence structure and portfolio allocation features. The copula counting technique aside from simplifying the analysis and interpretation of the assets’ dependence structure, it enables an in-depth and comprehensive analysis of their underlying dependence risk dynamics in specific market conditions. The average model convergence addresses the optimal stock selection and investment confidence problems underlying any type of portfolio optimization, and faced by investors when having to select stocks from a wide array of optimal investment scenarios, in a more objective manner, through model convergence and model consensus. Both, the copula counting technique and average model convergence are new concepts that introduce new theory to the pair vine copula and multiple risk measure-based portfolio optimization literatures. The research findings stemming from the vine copula modelling of dependence indicate that the each of the portfolios modelled has dependence risk features consistent with specific market conditions. Out of the seven portfolios modelled the gold mining and retail benchmark portfolios are found to have the lowest dependence risk in times of financial turbulence. The iron ore-nickel mining and oil-gas energy portfolios have the highest dependence risk in similar market conditions. Out of the energy portfolios the coal-uranium is significantly less dependence risky, relative to the oil-gas. Out of the mining portfolios the iron ore-nickel is the most dependence risky, while the gold portfolio has the lowest dependence risk. The retail benchmark portfolio is significantly less dependence risky than the manufacturing benchmark portfolio in both, tranquil periods and non-tranquil periods. In terms of investment risk, the oil-gas energy portfolio is the most risky. The “copula counting technique” is acknowledged for simplifying the analysis and interpretation of the portfolios’ dependence structure and their sectors’ dependence risk dynamics. The average model convergence provides an alternative avenue to identify stocks with large weight allocations and high return relative to risk. The research findings and empirical results are interesting in terms of theory and practical financial applications. Portfolio managers, risk managers, hedging practitioners, financial market analysts, systemic risk and capital requirement agents, who follow the trends of the Australian mining, energy, retail and manufacturing sectors, may find the obtained results useful to design investment risk and dependence risk-adjusted optimization algorithms, risk management frameworks and dynamic hedging strategies that best account for the downside risk the mining and energy sectors face during crisis periods to the pair vine copula and multiple risk measure-based portfolio optimization literatures. The research findings stemming from the vine copula modelling of dependence indicate that the each of the portfolios modelled has dependence risk features consistent with specific market conditions. Out of the seven portfolios modelled the gold mining and retail benchmark portfolios are found to have the lowest dependence risk in times of financial turbulence. The iron ore-nickel mining and oil-gas energy portfolios have the highest dependence risk in similar market conditions. Out of the energy portfolios the coal-uranium is significantly less dependence risky, relative to the oil-gas. Out of the mining portfolios the iron ore-nickel is the most dependence risky, while the gold portfolio has the lowest dependence risk. The retail benchmark portfolio is significantly less dependence risky than the manufacturing benchmark portfolio in both, tranquil periods and non-tranquil periods. In terms of investment risk, the oil-gas energy portfolio is the most risky. The “copula counting technique” is acknowledged for simplifying the analysis and interpretation of the portfolios’ dependence structure and their sectors’ dependence risk dynamics. The average model convergence provides an alternative avenue to identify stocks with large weight allocations and high return relative to risk. The research findings and empirical results are interesting in terms of theory and practical financial applications. Portfolio managers, risk managers, hedging practitioners, financial market analysts, systemic risk and capital requirement agents, who follow the trends of the Australian mining, energy, retail and manufacturing sectors, may find the obtained results useful to design investment risk and dependence risk-adjusted optimization algorithms, risk management frameworks and dynamic hedging strategies that best account for the downside risk the mining and energy sectors face during crisis periods

Australian mining industry: Credit and market tail risk during a crisis period

Industry risk is important to equities investors in determining portfolio mix. It is also important to lenders in managing credit portfolio risk. This article focuses on the mining industry in Australia, that country’s largest industry by exports. The study concentrates on extreme credit and market risk, to determine the riskiness of the mining industry relative to the broader market, with a focus on the Global Financial Crisis (GFC) period and the use tail risk metrics. These include Conditional Value at Risk (CVaR) for measuring market risk and Conditional Distance to Default (CDD) for measuring credit risk. Based on these metrics, the study finds market risk for mining shares to be higher than the broader market, but that the gap narrows during the crisis. From a credit perspective, despite higher volatility experienced by the mining industry, the default risk is lower than the broader market, due to the greater distance between mining entities’ asset and debt values

Fraud and Financial Markets: The 1997 Collapse of the Junior Mining Stocks

The Vancouver Composite Index fell by over 25% in less than six weeks during spring 1997 as the junior mining sector collapsed. We argue that this market collapse was triggered by the failure of Bre-X Minerals when that company’s Indonesian claims, previously believed to contain the world’s largest gold deposit, were shown to be pure fraud. Our event study, based on market returns for the Vancouver Composite Index and for a portfolio of 59 gold stocks, shows the effects of the Bre-X scandal to be both sizeable and significant. There is also some evidence that smaller exploration companies were hardest hit.Bre-X; event study; fraud; gold; mining

KINERJA PORTOFOLIO SAHAM PADA SEKTOR PERTAMBANGAN DI BURSA EFEK INDONESIA

The performance of stock portfolio observe not only return but also consider the risk of portfolio. There are several methods that can be used to measure the performance of portfolios using index Sharpe and Treynor index. Stock portfolio performance measurement can be facilitated by using a proxy that LQ 45, a liquid stock market capitalization is high, has a high-frequency trading, does not fluctuate and objectively has been selected by the Stock Exchange. The purpose of this research is to analyze the performance of mining stock portfolio using the Sharpe index and Treynor index in Indonesia Stock Exchange. This study tries to analyze the performance of mining stocks listed on the Indonesia Stock Exchange in the period 2011-2012 using the index Sharpe and Treynor index. The results showed that by using the index Sharpe and Treynor index no stock are performing well. All stock is negative. Performance ratings Sharpe index and Treynor index indicates that the stock portfolio ADRO ITMG is ranked one with an index Sharpe value of -0073 and Treynor index values -0.005.Keywords: Sharpe index, Treynor index, Stock, Portfolio Performance, LQ4