ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)

Regional underdevelopment and less developed business ecosystems: The case of Eastern Macedonia and Thrace

Charis Vlados, Dimos Chatzinikolaou, Fotios Katimertzopoulos and Theodore Koutroukis

Correspondence: Charis Vlados,

Democritus University of Thrace, Department of Economics, Greece

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This article aims to highlight the different facets of the relative socio-economic underdevelopment of the Greek region of Eastern Macedonia and Thrace. It explores initially regional analysis data, leading to the conclusion that the region does indeed face comparative weaknesses as it exhibits multiplier results and specialization in areas with the lowest value-added and employment. It then presents the main conclusions about small and micro firms of this less developed business ecosystem. It concludes that the region has structural competitiveness problems that are primarily due to the competitiveness of the firms that can host and nurture. The strengthening of competitiveness of this regional business ecosystem requires the improvement of the innovative potential that, in a triple helix condition, is the result of the evolutionary interconnection between local-regional firms, government, and academia. To this end, the proposal to establish a Local Development and Innovation Institute constitutes a new regional policy that can be applied to the region and strengthen the innovative potential of the entire regional business ecosystem.


  Underdeveloped regional business ecosystem, Eastern Macedonia and Thrace (REMTh), Stra.Tech.Man Lab research team, Regional triple helix, Local Development and Innovation Institutes


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Healthy. . .Distress. . . Default

Zura Kakushadze

Correspondence: Zura Kakushadze ,

Quantigic Solutions LLC, USA

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We discuss a simple, exactly solvable model of stochastic stock dynamics that incorporates regime switching between healthy and distressed regimes. Using this model, which is analytically tractable, we discuss a way of extracting expected returns for stocks from realized CDS spreads, essentially, the CDS market sentiment about future stock returns. This alpha/signal could be useful in a cross-sectional (statistical arbitrage) context for equities trading.


  stock; CDS spread; healthy; distress; default; stochastic dynamics; statistical arbitrage; alpha; regime switching; expected return; market sentiment; equities trading


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Does public indebtedness constrain or can it favor economic growth? A simple analytical modeling

Séverine MENGUY

Correspondence: Séverine MENGUY ,

Université Paris Descartes, France

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This paper aims at shedding an analytical light on the consequences of the public indebtedness level on economic growth. We show that increasing the current public debt can sustain short run economic activity, and mainly net exports and public investment expenditure. On the labor market, in the short run, the public debt can also increase the capital stock, the real wage, and more moderately labor demand and supply. However, our modelling also underlines that there are many obstacles to the positive effect of a higher public debt level on long term economic growth. Indeed, if the elasticity of the nominal interest rate to the increase of the public debt becomes high (worry of the financial markets), increasing the public indebtedness level can damage current economic activity. Besides, a previous increasing trend of the public debt can be damaging, and the trajectory of the public debt should also be taken into account.


  public debt, economic activity, public investment and consumption expenditure, private investment, private consumption, labor demand and supply.


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An Analysis of the Impact of Modeling Assumptions in the Current Expected Credit Loss (CECL) Framework on the Provisioning for Credit Loss

Michael Jacobs

Correspondence: Michael Jacobs,

PNC Financial Services Group, USA

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The CECL revised accounting standard for credit loss provisioning is intended to represent a for-ward-looking and proactive methodology that is conditioned on expectations of the economic cycle. In this study we analyze the impact of several modeling assumptions - such as the methodology for projecting expected paths of macroeconomic variables, incorporation of bank-specific variables or the choice of macroeconomic variables – upon characteristics of loan loss provisions, such as the degree of pro-cyclicality. We investigate a modeling framework that we believe to be very close to those being contemplated by institutions, which projects various financial statement line items, for an aggregated “average” bank using FDIC Call Report data. We assess the accuracy of 14 alternative CECL modeling approaches. A key finding is that assuming that we are at the end of an economic expansion, there is evidence that provisions under CECL will generally be no less procyclical compared to the current incurred loss standard. While all the loss prediction specifications perform similarly and well by industry standards in-sample, out of sample all models perform poorly in terms of model fit, and also exhibit extreme underprediction. Among all scenario generation models, we find the regime switching scenario generation model to perform best across most model performance metrics, which is consistent with the industry prevalent approaches of giving some weight to scenarios that are somewhat adverse. Across scenarios that the more lightly parametricized models tended to perform better according to preferred metrics, and also to produce a lower range of results across metrics. An implication of this analysis is a risk CECL will give rise to challenges in comparability of results temporally and across institutions, as estimates vary substantially according to model specification and framework for scenario generation. We also quantify the level of model risk in this hypothetical exercise using the principle of relative entropy, and find that credit models featuring more elaborate modeling choices in terms of number of variables, such as more highly parametricized models, tend to introduce more measured model risk; however, the more highly parametricized MS-VAR model, that can accommodate non-normality in credit loss, produces lower measured model risk. The implication is that banks may wish to err on the side of more parsimonious approaches, that can still capture non-Gaussian behavior, in order to manage the increase model risk that the introduction of the CECL standard gives rise to. We conclude that investors and regulators are advised to develop an understanding of what factors drive these sensitivities of the CECL estimate to modeling assumptions, in order that these results can be used in prudential supervision and to inform investment decisions. .


  Accounting Rule Change, Current Expected Credit Loss, Allowance for Loan and Lease Losses, Credit Provisions, Credit Risk, Financial Crisis, Model Risk.


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Machine Learning Risk Models

Zura Kakushadze and Willie Yu

Correspondence: Zura Kakushadze ,

Quantigic Solutions LLC, USA

pdf (1258.91 Kb) | doi:


We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions, including statistical risk models, risk models based on fundamental industry classifications, and also those utilizing multilevel clustering based industry classifications.


  machine learning; risk model; clustering; k-means; statistical risk models; covariance; correlation; variance; cluster number; risk factor; optimization; regression; mean-reversion; factor loadings; principal component; industry classification; quant; trading; dollar-neutral; alpha; signal; backtest


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Identifying Black Swans in the Athens Stock Exchange

Tsoukalas Asterios, Drimpetas Evaggelos and Geronikolaou George

Correspondence: Tsoukalas Asterios,

Head of Thessaloniki Regional Office of Hellenic Capital Market Commission, Greece

pdf (1258.91 Kb) | doi:


The purpose of this study is to identify Black Swans in the Athens Stock Exchange during a thirty years period from 1985 to 2015. Using a large dataset of daily returns, we point out that extraordinary returns are not rare and that Black Swans in the Greek Stock Market are more frequent than expected. We also to show that these outliers have an extreme impact on an investor’s long term return and finally that the normality assumption is not suitable in predicting the Black Swans phenomenon.


  Black Swans; Greek Stock Market; Normal Distribution


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Altcoin-Bitcoin Arbitrage

Zura Kakushadze and Willie Yu

Correspondence: Zura Kakushadze,

Quantigic Solutions LLC, USA

pdf (1258.91 Kb) | doi:


We give an algorithm and source code for a cryptoasset statistical arbitrage alpha based on a mean-reversion effect driven by the leading momentum factor in cryptoasset returns discussed in Using empirical data, we identify the cross-section of cryptoassets for which this altcoin-Bitcoin arbitrage alpha is significant and discuss it in the context of liquidity considerations as well as its implications for cryptoasset trading.


  cryptoasset, cryptocurrency, altcoin, Bitcoin, mean-reversion, momentum, statistical arbitrage


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Kakushadze, Z. (2015) 4-Factor Model for Overnight Returns. Wilmott Magazine 2015(79): 56-62. Available online:

Kakushadze, Z. (2018) Cryptoasset Factor Models. Algorithmic Finance (forthcoming). Available online:

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Kim, Y.B., Kim, J.G., Kim, W., Im, J.H., Kim, T.H., Kang, S.J. and Kim, C.H. (2016) Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies. PLoS ONE 11(8): e0161197.

Kristoufek, L. (2015) What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis. PLoS ONE 10(4): e0123923.

Lee, D.K.C., Guo, L. and Wang, Y. (2018) Cryptocurrency: A New Investment Opportunity? Journal of Alternative Investments 20(3): 16-40.

Li, T.R., Chamrajnagar, A.S., Fong, X.R., Rizik, N.R. and Fu, F. (2018) Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model. Working Paper. Available online:

Liew, J.K.-S. and Hewlett, L. (2018) The Case for Bitcoin for Institutional Investors: Bubble Investing or Fundamentally Sound? Working Paper. Available online:

Liew, J.K.-S., Li, R.Z. and Budavári, T. (2018) Crypto-Currency Investing Examined. Working Paper. Available online:

Nakano, M., Takahashi, A. and Takahashi, S. (2018) Bitcoin Technical Trading With Artificial Neural Network. Working Paper. Available online:

Ortisi, M. (2016) Bitcoin Market Volatility Analysis Using Grand Canonical Minority Game. Ledger 1: 111-118.

Shah, D. and Zhang, K. (2014) Bayesian regression and Bitcoin. Working Paper. Available online:

Sharpe, W.F. (1994) The Sharpe Ratio. Journal of Portfolio Management 21(1): 49-58.

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Evaluating interdependencies in African markets A VECM approach

Konstantinos Vergos and Benjamin Wanger

Correspondence: Konstantinos Vergos,

Portsmouth Business School, United Kingdom

pdf (1258.91 Kb) | doi:


This study evaluates the linkages between stock markets and macroeconomic data in the sub-Sahara Africa during the 2008 –2018 period by using VECM. Our findings confirm unidirectional and bidirectional causalities, and a long-run equilibrium between the indexes, the stock exchanges and their national economies. The contemporaneous sectoral infectivity surpasses the long-run responses. While the banking sector was found to lead markets and macroeconomic indices, Nigerian, Moroccan and Swaziland markets were found to be most weakly integrated. Our findings provide a unique evidence of interdependence between African peripheral markets that could be used in cross-hedging and speculative strategies in fund management.


  Asset pricing, Africa, interdependence, VECM


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Credit Risk Determinants: Evidence from the Bulgarian Banking System

Petros Golitsis, Athanasios P. Fassas and Anna Lyutakova

Correspondence: Athanasios P. Fasas,

University of Thessaly, Greece

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The present study examines a wide set of credit risk determinants for the Bulgarian banking system. Using both monthly and quarterly data and employing two methodologies, Vector Autoregressive and Autoregressive Distributed Lag models, we test ninety-one possible determinants of the banks’ credit risk, as measured by non-performing loans, loan loss provisions and problematic loans. Our empirical findings show that both bank-specific and institutional, in addition to macroeconomic, factors have a significant impact on the credit risk of the banking system in the country.


  credit risk; non-performing loans; loan loss provisions; Bulgarian banking system.


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Stock Market and Macroeconomic Policies in Mongolia

Hiroyuki Taguchi and Namjil Enkhbaatar

Correspondence: Hiroyuki Taguchi,

Graduate School of Humanities and Social Sciences, Saitama University, Japan

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Mongolian stock market is underdeveloped compared with its banking credit market, due to a lot of impediment factors to prevent its development. This means Mongolian economy still has much room where its stock market development promotes the long-term financing and investment into non-mining sectors for sustainable economic growth. This paper aims to provide the evidence on the relationship between stock market and macroeconomic policies in Mongolia under the hypothesis that the recent biases of fiscal and monetary policies would distort her stock-price formation. The empirical analysis in this study found that the cumulative public debt and too high policy rate have stagnated the stock prices, through identifying the negative impulse responses of stock prices to the shocks of policy rate and government securities under a vector-autoregressive model estimation. The strategic policy implication for normalizing the stock prices could be the significance in ensuring budget consolidation and in addressing a fear of floating in monetary policy management in Mongolia.


  Stock Market; Fiscal policy; Government Securities; Monetary Policy; Policy Rate.


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