Abstract: The green bond market is attracting new issuers and a more diversified base of investors. However, the size of the green bond market remains small compared to the challenges it is meant to address and to the overall traditional bond market. This paper is based on a unique methodology combining an extensive literature review, market data analysis, and interviews with a large spectrum of green bond market participants. We identify the current barriers explaining the lack of scalability of the green bond market: a deficit of harmonized global standards; risks of greenwashing; the perception of higher costs for issuers; the lack of supply of green bonds for investors; and the overall infancy of the market. This paper makes several recommendations to overcome these obstacles and unlock the full potential of green bonds to finance sustainability goals.

Abstract: Digital and information technologies (IT) are becoming silently pervasive in old-fashioned real estate markets. This paper focuses on three important avenues for the diffusion of IT in commercial real estate: online brokerage and sales, the commoditization of space and Fintech in mortgage and equity funding. We describe the main new markets and products created by this IT revolution. The focus is on the pioneering US market, with some attention devoted to the specific firms and institutions taking these innovations into the mainstream. We also carefully analyze the economic underpinnings from which the new technologies can expect to generate cash flows, thus becoming viable—or not. Finally, we discuss their likely impact on established players in the commercial real estate arena.,In this paper, the author chooses to focus on three separate arenas where the IT revolution—sometimes referred to as Proptech, as applied to real estate—is having discernible impacts: sales and brokerage, space commoditization and online finance platforms. The author invites the reader to think seriously about the economic fundamentals that may—or may not—sustain new business models in Proptech. Real estate economists and investors alike need to be critical of new business models, especially when they are being aggressively marketed by their promoters. Trying to avoid any hype, the author provides thoughts about the likely impact of the innovations on their markets, guided by economic and finance theory, and previous experience.,The author evaluates the evolution of commercial real estate brokerage. While innovations will, no doubt, have an impact on the ways in which we buy and lease commercial properties, the lessons from the housing market should make us skeptical about the possibility of the new technologies dramatically facilitating disintermediation in this market. In fact, new oligopolies seem to be emerging with regard to market data provision.,Proptech will change some aspects of the real estate industry, but not others!,As change pervades the property industry, only a relatively few research pieces are illustrating or—more importantly—providing insights about the likely economic and financial impacts of IT penetration. Similarly, only a few papers have so far addressed the economic viability of the alternative business models of tech startups targeting real estate markets and transactions.

Abstract: This paper aims to investigate the relation between corporate governance and dividend policy in Sri Lankan firms.,The data set consists of market data using 1,608 firm-year observations from 201 firms listed on the Colombo Stock Exchange and survey-based data from 151 respondents from the same 201 firms. The authors use data triangulation to examine the two approaches.,The analysis of the market data reveals that a significantly positive relation between corporate governance on both the propensity to pay dividends and dividend payout. Survey analysis confirms these findings. Triangulated evidence supports the outcome model of dividends, free cash flow and agency cost theories.,The findings are useful not only for management in developing suitable corporate governance practices and dividend policies for their firms but also for shareholders in evaluating both existing and new investments. Future researchers should investigate the same phenomenon in other contexts using triangulation approaches to confirm their findings.,This study is the first to use governance indices both in terms of survey and market-based data to examine the relation between corporate governance and dividend policy.

Abstract: With the development of cloud computing and big data, stock prediction has become a hot topic of research. In the stock market, the daily trading activities of stocks are carried out at different frequencies and cycles, resulting in a multi-frequency trading mode of stocks , which provides useful clues for future price trends: short-term stock forecasting relies on high-frequency trading data, while long-term forecasting pays more attention to low-frequency data. In addition, stock series have strong volatility and nonlinearity, so stock forecasting is very challenging. In order to explore the multi-frequency mode of the stock , this paper proposes an adaptive wavelet transform model (AWTM). AWTM integrates the advantages of XGboost algorithm, wavelet transform, LSTM and adaptive layer in feature selection, time–frequency decomposition, data prediction and dynamic weighting. More importantly, AWTM can automatically focus on different frequency components according to the dynamic evolution of the input sequence, solving the difficult problem of stock prediction. This paper verifies the performance of the model using S&P500 stock dataset. Compared with other advanced models, real market data experiments show that AWTM has higher prediction accuracy and less hysteresis.

Abstract: In this study, we use entropy-based measures to identify different types of trading behaviors. We detect the return-driven trading using the conditional block entropy that dynamically reflects the “self-causality” of market return flows. Then we use the transfer entropy to identify the news-driven trading activity that is revealed by the information flows from news sentiment to market returns. We argue that when certain trading behavior becomes dominant or jointly dominant, the market will form a specific regime, namely return-, news- or mixed regime. Based on 11 years of news and market data, we find that the evolution of financial market regimes in terms of adaptive trading activities over the 2008 liquidity and euro-zone debt crises can be explicitly explained by the information flows. The proposed method can be expanded to make “causal” inferences on other types of economic phenomena.

Abstract: In the current low-interest market environment, more and more asset managers have started to consider to invest in property markets. To implement adequate and forward-looking risk management procedures, this market should be analyzed in more detail. Therefore, this study aims to examine the housing market data from the UK. More specifically, sentiment data and house prices are examined, using techniques of time-series econometrics suggested by Toda and Yamamoto (1995). The monthly data used in this study is the RICS Housing Market Survey and the Nationwide House Price Index – covering the period from January 2000 to December 2018. Furthermore, the authors also analyze the stability of the implemented Granger causality tests. In sum, the authors found clear empirical evidence for unidirectional Granger causality from sentiment indicator to the house prices index. Consequently, the sentiment indicator can help to forecast property prices in the UK.,By investigating sentiment data for house prices using techniques of time-series econometrics (more specifically the procedure suggested by Toda and Yamamoto, 1995), the research question whether sentiment indicators can be helpful to predict property prices in the UK is analyzed empirically.,The empirical results show that the RICS Housing Market Survey can help to predict the house prices in the UK.,Given these findings, the information provided by property market sentiment indicators certainly should be used in a forward-looking early warning system for house prices in the UK.,To authors’ knowledge, this is the first paper that uses the procedure suggested by Toda and Yamaoto to search for suitable early warning indicators for investors in UK real estate assets.

Abstract: Abstract The Research Data Centre at the Institute for Employment Research (RDC-IAB) has been offering high-quality administrative and survey data on the German labour market for 15 years and has become one of the most important locations worldwide for researchers interested in data for labour market research. This article provides an overview of the RDC-IAB, including its data and access modes. The article presents two datasets in more detail: the Sample of Integrated Employment Biographies, a classic dataset, and the Linked Personnel Panel, a new dataset. Finally, this article provides insights into future infrastructure and data developments.

Abstract: This paper studies manipulation in cash-settled derivative contract markets. When traders hedge factor risk using cash-settled derivatives, which are settled based on the price of a spot good, traders can manipulate settlement prices by trading the spot good. In equilibrium, manipulation can make all agents worse off. I define two measures of manipulation-induced welfare losses, which can be estimated using commonly observed market data. Using these measures, I estimate how large manipulation-induced distortions would be if COMEX gold futures were cash-settled using the London Bullion Market Association gold price benchmark.

Abstract: In this paper, we estimate the probability of a financial institution breaching the Common Equity Tier 1 capital under Basel III rules. We do so by applying the Merton model, where balance sheet data and market data are used to match the probability of default implied by the model with the probability of default implied by market quotations for credit default swaps. We provide an empirical analysis for several banks classified by the Financial Stability Board and the Basel Committee on Banking Supervision as Global Systemically Important Financial Institutions, evaluating how the probability of breaching the Common Equity Tier 1 Capital evolved from 2005 to 2015. We find that higher Common Equity Tier 1 Capital ratios do not necessarily imply lower probabilities of breaching capital requirements and vice versa. We also focus on the asset volatility calibrated according to our model and we find that it appears to be a good proxy for the risk-weighted asset density.

Abstract: The paper models evolution in pecunia—in the realm of finance. Financial markets are explored as evolving biological systems. Investors pursuing diverse investment strategies compete for the market capital. Some `survive' and some `become extinct.' A central goal is to identify evolutionary stable, i.e. guaranteeing survival, investment strategies. The problem is studied in a framework combining stochastic dynamics and evolutionary game theory. The model proposed employs only objectively observable market data, in contrast with traditional settings relying upon unobservable investors characteristics (utilities and beliefs). The main result is a construction of an evolutionary stable strategy in the model at hand.

Abstract: The fundamental principle in Modern Portfolio Theory (MPT) is based on the quantification of the portfolio's risk related to performance. Although MPT has made huge impacts on the investment world and prompted the success and prevalence of passive investing, it still has shortcomings in real-world applications. One of the main challenges is that the level of risk an investor can endure, known as \emph{risk-preference}, is a subjective choice that is tightly related to psychology and behavioral science in decision making. This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization on the mean-variance portfolio allocation framework. Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data. We demonstrate our methods on real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings. Moreover, the quantified risk preference parameters are validated with two well-known risk measurements currently applied in the field. The proposed methods could lead to practical and fruitful innovations in automated/personalized portfolio management, such as Robo-advising, to augment financial advisors' decision intelligence in a long-term investment horizon.

Abstract: Recently, the general trend in the airline industry has been to generate ancillary revenue by offering additional services. Instead of completely separating ancillary services from tickets as optional components, most of the traditional airlines offer the so-called branded fares which bundle some of the ancillary components to an inclusive fare preventing a possible negative impact on the customers’ perception and brand image (mixed bundling). For instance, seat reservation and baggage transportation are often already included in the default fare. In this study, we analyse data to evaluate different bundle-pricing policies within the mixed bundling context. We use statistical regression methods to infer individual behaviour by analysing aggregated data on market level from a major European airline. We tackle the question of how to optimally price bundled fares. With the General Data Protection Regulation in place today, such high-level models which only require aggregated market data and no individual personal data are becoming more relevant for business analytics. We demonstrate how aggregate data still allow to investigate individual behaviour and our data analysis reveals the existence and variability of price elasticity. The results can help companies to segment their markets based on price elasticity and optimise their ancillary offerings accordingly.

Abstract: At first glance, the portfolio management strategy seems like a resolved question, but practitioners continue to perform poorly on the stock markets. This paper highlights the portfolio management in the specific case of the West African regional stock exchange, regarding two management strategies. These are dynamic strategy and passive strategy. Within this framework, we will compare an investor who is constantly betting on price fluctuations with another who is betting on dividends. Its originality lies in the approach that is used. Through a simulation methodology based on real market data, the main results indicate that an emerging market is a savings market more than it is a speculation market. Besides, other results indicate that, one can predict on the West African regional stock exchange tomorrow’s prices from today’s prices. This does not mean that investors are making good predictions because the predictability of prices is due to the absence of changes in asset prices on the market. We draw the conclusion that it is difficult for one speculator to outperform the other. A rational investor would benefit from anticipating the distribution of dividends rather than focusing on price fluctuations. Consequently, the buy and hold strategy is therefore the best to be rewarded in an emerging market. Nonetheless, this practice can lead to a decline in liquidity.

Abstract: As rapid growth, Chinese financial derivative market is holding increasingly large proportions in entire domestic capital market as well as in global shares. To the nature of derivative instruments, plenty of market data features (such as prices and trading volumes) and off-market factors (such as financial news and policies) can directly impact on the price and risk in Chinese financial derivative markets, which is becoming more and more infeasible to model by using only traditional financial models and hand-crafted features. To alleviate the issue, in this paper we introduce some state-of-art deep neural network architectures and model two significant futures market price and risk indicators that are widely used by Chinese regulators, which are turn-over ratio (ratio of daily trading volumes and daily open interest volumes) and price basis (gap between futures price and corresponding spot product price). The extensive experimental results show that deep learning methods perform better prediction accuracy than traditional methods, among which convolutional LSTM achieves better results in most cases as it can capture local time-variant patterns. In addition, we also propose methods to exploit alternative off-market features (such as social media emotions and Baidu Search Index) with DNN models, which are proven beneficial to the price and risk prediction by rendering extra information than only market data.

Abstract: Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of crowding on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. We propose direct metrics of crowding that capture the presence of investors contemporaneously trading the same stock in the same direction by looking at fluctuations of the imbalances of trades executed on the market. We identify significant signs of crowding in well known equity signals, such as Fama-French factors and especially Momentum. We show that the rebalancing of a Momentum portfolio can explain between 1-2% of order flow, and that this percentage has been significantly increasing in recent years.

Abstract: To model the nonlinear and complex dynamics of financial systems, a new model for the formation of financial prices is developed, taking into account heterogeneity in the communication range of market agents. Specifically, one type of agents can potentially gather and disseminate information via additional long-distance contacts compared to the other type, and interactions among these agents are imitated by the contact process. The financial price series of the model are simulated, analysed, and compared with multiple major stock indices in nonlinear fluctuation behaviours. To better investigate the complexity structure of the financial time series, a generalization of the multiscale entropy method is developed to consider various moments in coarse graining. Overall, the modelled series are found to follow a fat-tail distribution and a pattern of complexity structure over both moments and time scales similar to real market data. This similarity is also shown by applying alternative complexity measure, matching energy method. Moreover, the wealth inequality among agents is found to increase over time within each type as well as across two types, further revealing nonlinear price and welfare dynamics of the model.

Abstract: We study linkages between stock exchanges’ proprietary data sales and trading activity by analyzing the introduction of a new data product, New York Stock Exchange’s Integrated Feed (NYSE IF). Consistent with trading and information on trading being complements, firms that subscribed to NYSE IF increased their share of trading on NYSE. In principle, firms subscribing to NYSE IF could impose a negative externality on non-subscribing firms due to increased information asymmetry. However, consistent with information purchases having a positive network externality due to increased liquidity, non-subscribing firms also increased their share of trading on NYSE. These findings are relevant to our understanding of competition among stock exchanges, complementarity between market data and trading, and policy questions relating to the pricing of market data.

Abstract: We establish support and resistance levels from data in intraday currency exchange market activity based on machine learning methods. Specifically we design two semi-supervised classification neural networks. The first one is based on a variant of the K-means method while the second is based on a Gaussian mixture model with expectation maximisation. Each performs classification from tick data on very short time windows and produces the corresponding support and resistance price levels. We test the methodology on actual market data for the EUR-USD currency exchange. As a sanity check we also perform mock trades based on actual market data. We evaluate the results for statistical significance using a number of performance metrics while also comparing against traditional methods.

Abstract: The calibration of financial models is a laborious, time-consuming and expensive task, which needs to be performed frequently by financial institutions. Recently, the application of artificial neural networks (ANNs) for calibration has gained interest. This paper provides the first comprehensive empirical study on the application of ANNs for calibration based on observed market data. We benchmark the performance against a real-life calibration framework. We show that the results of an ANN based calibration framework are very competitive and derive guidelines for its practical implementation to enhance and accelerate managerial decisions. Furthermore, we show that our calibrated parameters are more stable over time, enabling more reliable risk reports and business decisions.

Abstract: Investors try to predict returns of financial assets to make successful investment. Many quantitative analysts have used machine learning-based methods to find unknown profitable market rules from large amounts of market data. However, there are several challenges in financial markets hindering practical applications of machine learning-based models. First, in financial markets, there is no single model that can consistently make accurate prediction because traders in markets quickly adapt to newly available information. Instead, there are a number of ephemeral and partially correct models called "alpha factors". Second, since financial markets are highly uncertain, ensuring interpretability of prediction models is quite important to make reliable trading strategies. To overcome these challenges, we propose the Trader-Company method, a novel evolutionary model that mimics the roles of a financial institute and traders belonging to it. Our method predicts future stock returns by aggregating suggestions from multiple weak learners called Traders. A Trader holds a collection of simple mathematical formulae, each of which represents a candidate of an alpha factor and would be interpretable for real-world investors. The aggregation algorithm, called a Company, maintains multiple Traders. By randomly generating new Traders and retraining them, Companies can efficiently find financially meaningful formulae whilst avoiding overfitting to a transient state of the market. We show the effectiveness of our method by conducting experiments on real market data.

Abstract: Using machine learning and alternative data for the prediction of financial markets has been a popular topic in recent years. Many financial variables such as stock price, historical volatility and trade volume have already been through extensive investigation. Remarkably, we found no existing research on the prediction of an asset's market implied volatility within this context. This forward-looking measure gauges the sentiment on the future volatility of an asset, and is deemed one of the most important parameters in the world of derivatives. The ability to predict this statistic may therefore provide a competitive edge to practitioners of market making and asset management alike. Consequently, in this paper we investigate Google News statistics and Wikipedia site traffic as alternative data sources to quantitative market data and consider Logistic Regression, Support Vector Machines and AdaBoost as machine learning models. We show that movements in market implied volatility can indeed be predicted through the help of machine learning techniques. Although the employed alternative data appears to not enhance predictive accuracy, we reveal preliminary evidence of non-linear relationships between features obtained from Wikipedia page traffic and movements in market implied volatility.

Abstract: Recently, many uses of artificial intelligence have appeared in the commercial field. Artificial intelligence allows computers to analyze very large amounts of information and data, reach logical conclusions on many important topics, and make difficult decisions, this will help consumers and businesses make better decisions to improve their lives, and it will also help startups and small companies achieve great long-term success. Currency exchange rates are important matters for both governments, companies, banks and consumers. The decision tree is one of the most widely artificial intelligence tools used in data mining. With the development of this field the decision tree and Gradient boosting decision tree are used to predicate through constructed intelligent predictive system based on it. These algorithms have been used in many stock market forecasting systems based on global market data. The Iraqi dinar exchange rates for the US dollar are affected in local markets, depending on the exchange rate of the Central Bank of Iraq and the features of that auction. The proposed system is used to predict the dollar exchange rates in the Iraq markets Depending on the daily auction data of the Central Bank of Iraq (CBI). The decision tree and Gradient boosting decision tree was trained and testing using dataset of three-year issued by the CBI and compare the performance of both algorithms and find the correlation between the data. (Runtime, accuracy and correlation) criteria are adopted to select the best methods. In system, the characteristic of artificial intelligence have been integrated with the characteristic of data mining to solve problems facing organization to use available data for decision making and multi-source data linking, to provide a unified and integrated view of organization data.

Abstract: Volatility clustering and fat tails are prominently observed in financial markets. Here, we analyze the underlying mechanisms of three agent-based models explaining these stylized facts in terms of market instabilities and compare them on empirical grounds. To this end, we first develop a general framework for detecting tail events in stock markets. In particular, we introduce Hawkes processes to automatically identify and date onsets of market turmoils which result in increased volatility. Second, we introduce three different indicators to predict those onsets. Each of the three indicators is derived from and tailored to one of the models, namely quantifying information content, critical slowing down or market risk perception. Finally, we apply our indicators to simulated and real market data. We find that all indicators reliably predict market events on simulated data and clearly distinguish the different models. In contrast, a systematic comparison on the stocks of the Forbes 500 companies shows a markedly lower performance. Overall, predicting the onset of market turmoils appears difficult, yet, over very short time horizons high or rising volatility exhibits some predictive power.

Abstract: Technical analysis is one of the financial market analysis tools. Technical analysis is a method of anticipating prices and markets through studying historical market data. Based on the factors studied in this type of analysis, indicators are designed and presented to facilitate decision-making on buy and sell stress and then buy and sell action in financial markets. This research evaluates performances and returns of 10 conventional technical analysis indicators based on the strategies set on the total stock exchange index, the total index of OTC market and 8 other (non-correlated) industry indices by using Meta Trader software from 2008 to 2018. Also, the significance of the difference between the returns of the indicators is tested using the buy and hold strategy. The results show a significant difference between the returns using some of the technical analysis indicators in some indices and buy and hold strategy. The effectiveness of technical analysis strategies varies across industries and EMA and SMA with respectively 6 and 5 repetitions, are the best strategies and BB with just one repetition has the least repetition. The investment industry index with the most repetition is the industry in which the strategies used in this study have been able to provide an acceptable return.

Abstract: The price of proprietary market data, data with low latency and with complete depth of book, sold by exchanges has risen dramatically in the previous decade. In fact, in October, 2018, the SEC failed to approve a requests by NASDAQ and NYSE-ARCA to raise the price of their data. The paper conceptually analyzes the nature of the demand for proprietary data and concludes that, for a significant part of the market, data from different exchanges are complementary--buying NASDAQ proprietary data increases the usefulness of NYSE-ARCA data. As a consequence, we should not expect competition between the 13 exchanges to constrain prices. This is in contrast to net trading fees, which are driven by competition to reasonable levels.