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This chapter introduces the fundamentals of portfolio and financial consumer protection from frauds in the cryptoasset space. Cryptoassets pose new risks to portfolios and financial consumers: idiosyncratic risks stemming from their unique features and systematic risks arising from transitioning from centralized to decentralized finance. Market experience indicates that these risks threaten every portfolio and financial consumer holding cryptoassets. In the consumer protection framework, cryptoasset risks are higher than traditional asset risks. Cryptoassets fall outside the regulatory domain in many jurisdictions. Moreover, their decentralized nature, technological attributes, and the momentum of financial technology cause asymmetric technology, disarming system-based portfolio and consumer protection mechanisms against frauds and abuses. Hence, the idiosyncratic and systematic risks of cryptoassets highlight the importance of developing more vigilant self-protection mechanisms.

Bitcoin’s introduction as the first cryptoasset in 2009 ushered in a new era, representing a seismic shift in the financial markets. Since then, this evolving asset class has generated much interest, excitement, and growth. This chapter begins by providing a brief background of cryptoassets. It then discusses their main types (cryptocurrencies, security tokens, and utility tokens), users (individual investors, major financial institutions, endowments, and hedge funds), and benefits and drawbacks. Next, it sets forth the book’s purpose, distinguishing features, intended audience, and structure. The chapter provides a synopsis of each of the remaining 21 chapters. Although no single book can encompass all changes and iterations of these technologies as they emerge in the marketplace, this book brings together a broad collection of industry expertise and academic analysis to create a book helpful to researchers, academics, and practitioners.

Despite the quantity of collaborations, the vocational network of the housing production in Ankara during its first five years (1923–1928) remains dispersed. The aim of this study is to identify all the actors of housing production and their collaborations which shaped Ankara's urban development as the new capital city.

The study engages with the literature and archival documents to identify the actors of the housing production, i.e. architects, master-builders, public institutions, private companies, contractors and entrepreneurs, and their resultant vocational network in the housing production in Ankara during 1923–1928.

Due to different agendas, such as speculation, financial interests or patriotism, the construction industry in Ankara had become an arena where many paths intersected, forming an intertwined vocational network. The profession of contractor became popular, and local architects, engineers and even individuals of various other professions began to work as mediators for foreign companies and public institutions, which required support especially in large-scale projects.

The dispersed information revealed that the actors of the housing production remained mostly anonymous, or only the famous architects were commemorated; however, others could be found within the lines of the established literature on Ankara and/or in archival documents. This research not only focuses on “salient” actors but also highlights the “silent” actors of the housing production and prepares charts to clarify the vocational network in Ankara during its first five years to contribute to the future studies on Ankara and its housing.

The main purpose of this study is to detect homogeneous housing market areas among 196 districts of 5 major cities of Turkey in terms of house sale price indices. The second purpose is to forecast these 196 house sale price indices.

In this paper, the authors use the monthly house sale price indices of 196 districts of 5 major cities of Turkey. The authors propose an autoregressive (AR) model-based fuzzy clustering approach to detect homogeneous housing market areas and to forecast house price indices.

The AR model-based fuzzy clustering approach detects three numbers of homogenous property market areas among 196 districts of 5 major cities of Turkey where house sale price moves together (or with similar house sales dynamic). This approach also provides better forecasting results compared to standard AR models by higher data efficiency and lower model validation and maintenance effort.

In this study, the authors could not use any district-based socioeconomic and consumption behavioral indicators and any discrete geographical and property characteristics because of the data limitation.

The finding of this study would help property investors for establishing more effective property management strategies by taking different geographical location conditions into account.

From the government side, knowing future rises, falls and turning points of property prices in different locations can allow the government to monitor the property price changes and control the speculation activities that cause a dramatic change in the market.

There is no previous research paper focusing on neighborhood-based clusters and forecasting house sale price indices in Turkey. At this point, it is the first academic study.

This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.

The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.

The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.

This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.

The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.