Camilla Mazzoli

Intellectual Capital Disclosure in IPOs: Is It Worth It?

We study the association between Intellectual Capital (IC) disclosure and IPO results for a sample of firms that have listed on the Italian stock market. By making use of an in-depth content analysis of the non-financial information disclosed in the listing prospectus, we disentangle the effects of such a disclosure on the underpricing from those effects that are produced on the price adjustment. Our empirical findings suggest an overall positive effect of the IC disclosure on IPO results as the increase in the underpricing, documented by previous literature as an opportunity cost, is preceded by an upward adjustment of the offer price, which means that more money was raised. Moreover, we provide evidence that primary and secondary market investors appreciate non-financial information in different ways. Our results encourage firms that are going public to definitely engage in disclosure of IC as a means to improve IPO results.

Intellectual Capital Disclosure and IPO Results: Is It a Matter of Classification?

We study the effects of Intellectual Capital (IC) disclosure on the IPO results. Previous findings provide inconsistent results, possibly due to the distinct ways in which IC is classified. We apply two different IC classifications to the information disclosed in the listing prospectuses of 74 Italian IPOs issued between 2004 and 2014 and we build different IC disclosure indexes. The indexes are put into a series of regressions regarding the IPO results and then quantified in terms of price adjustment and underpricing. We demonstrate that the effects of IC disclosure on the IPO results are comprehensively consistent across the different IC classifications, although some differences emerge. Such evidence make clear to listing firms the great benefits to be derived from the proper disclosure of their non-financial assets to investors.

Risk Profiling and Current Suitability Practices: What Can Be Learned from a Sample of Italian Householders

According to the Markets in Financial Instruments Directive (MiFID), financial intermediaries are requested to assess the suitability of the products they sell to clients, particularly in respect of retail clients. Supervisory Authorities have pointed out that one of the main problems in the practical implementation of the MiFID suitability rule stems from the omission or impreciseness of the questions addressed to know the customer’s characteristics with specific reference to their risk profile. With this paper we try to shed light on the information an intermediary should collect from customers in order to properly define their risk profile. We analyze a sample of 2995 suitable portfolios and we put their risk composition in relation with some characteristics of the owner. By using the Heckit two-steps estimation procedure we are also able to set apart the variables that mainly explain the risk-holding decision (whether to acquire risky assets) and the risk-allocation decision (what fraction of wealth to invest in these assets). We find that the former decision may be induced by a smaller set of variables, essentially related to the capability of understanding and emotionally bearing the risk, while the latter is more related to the individual economic and financial capacity.

The Traditional Approach to Risk Tolerance

Assessing client risk tolerance is one of the most important, yet most nebulous, activities within the financial planning process. A person’s level of risk tolerance impacts upon a diverse number of financial decisions, such as portfolio management, type of mortgage, insurance deductibles, estate planning, and so on (Roszkowski and Grable, 2005). For years, the need to understand the attitude of investors toward risk has led both academics and practitioners to find reliable tools to assess the risk tolerance of individuals.

An Insight into Suitability Practice: Is a Standard Questionnaire the Answer?

For years, financial intermediaries have been focusing on data and information obtained from customers in order to develop and carry out their Customer Relationship Management (CRM) techniques; they have created and improved models that allow them to split customers into homogeneous groups according to some specific drivers (see Channon, 1985; Anderson and Kerr, 2002; Peelen, 2005). Knowing a customer’s characteristics is crucial to intermediaries for two main reasons: from an ex-ante perspective, they can create products and services which can be specifically marketed and sold to particular segments of customers (customer segmentation); ex post, they make use of the information they obtain from clients in order to provide them with products and services that are suitable to their profile, thus reducing complaints and granting a good level of loyalty (customer profiling). In the literature, much effort is devoted to explaining the main drivers of customer segmentation, while the work on customer profiling is less developed. Nonetheless, the profiling process that financial firms follow to sell financial products and services is crucial, as it paves the way for a fiduciary relationship between the customer and the intermediary. Moreover, it has implications in terms of protecting both the investor and the intermediary. In fact, the information that is obtained from the customer in the profiling process is crucial in each step of the contract: a) before the financial service or product is subscribed, in order to meet the preferences and needs of the investors; b) during the contract, in order to acquire any changes in the needs and preferences of the investor; c) after the end of the contract, in order to protect the intermediary against any complaint that the client could make with reference to a loss that he or she did not expect.

The Role of Risk in the Investment Decision Process: Traditional vs Behavioural Finance

Risk and uncertainty exist whenever the future is unknown (Williams et al., 1997). Despite the intuitiveness of the concept, risk and its definition are still a debated issue in the literature. A first point to be discussed is the difference between risk and uncertainty. Knight (1921) maintains that risk refers to a situation for which the possible outcomes and their probabilities are known (measurable uncertainty); on the contrary, uncertainty exists when the probabilities associated to the outcomes are not known (unmeasurable uncertainty). In any case, not all academic authors agree on this distinction. Levy and Sarnat (1995) point out that, if one considers the existence of a subjective probability according to Savage (1954), the distinction between risk and uncertainty disappears. In fact, by assigning an individual subjective probability to decision problems, an uncertain situation can be transformed into a risky choice1 and the distinction loses significance.

Il pricing della consulenza in materia di investimento in Italia

I livelli di cultura finanziaria degli investitori, non sempre sufficienti a comprendere e valutare pienamente gli strumenti finanziari ed i servizi di investimento loro proposti dagli intermediari, rendono determinante il ruolo della consulenza finanziaria nell’ambito dell’asset management. Il recepimento della direttiva Mifid ha incrementato l’interesse, gia evidenziato in letteratura, in tema di consulenza finanziaria; in particolare, l’attenzione e stata rivolta al riconoscimento giuridico del consulente indipendente ed alla gestione dei conflitti di interesse che possono insorgere sia nell’attivita di consulenza che in quella di distribuzione di prodotti e servizi finanziari. Nel lavoro gli autori presentano i risultati di una verifica empirica condotta sul mercato italiano al fine di individuare le logiche di formazione e le strutture di pricing della consulenza, verificando le differenze esistenti con i modelli di pricing adottati nei principali mercati internazionali (in particolare Usa ed Australia). I risultati evidenziano come la forma organizzativa del consulente rappresenti il principale elemento di condizionamento del pricing, che risulta influenzato in modo determinante anche dalle dimensioni dell’advisor e dal suo grado di preparazione. Dal confronto internazionale emerge una forte eterogeneita dei modelli di pricing, solo in parte da attribuire a vincoli di carattere normativo e regolamentare.

Pre-Trade Transparency on the Italian Stock Exchange: A Trade Size Model on Panel Data

The purpose of this study was to analyze the effects that have been caused by changes in pre-trade transparency upon the behavior of stock traders. We used a trade size model and tested it before, during and after the period when the Italian Stock Exchange introduced a 20-level order book with disaggregated orders. Tick by tick data of the whole set of stocks (up to 277) listed on the Italian Stock Exchange were studied through fixed-effects panel models, within intra day (every 30 minutes and 150 minutes) and daily time frames. Our results indicate that order flows, bidask spreads, levels of risk and some information events differentially affect trade sizes when investors receive better information prior to negotiation. Both (intra day) informed and uninformed traders operating in a more transparent market became more reticent, with reduced trades sizes and higher orders’ cancellations. Moreover, it appears that the higher degree of order book disclosure permits traders to downsize their level of risk aversion; i.e. it reduces the ’uncertainty’ that would otherwise result in disrupted trading activity under conditions of information opacity.

Innovation in Trading Activity: Should Stock Markets Be More Transparent?

This valuable book discusses in detail, through a blend of theory and empirical research, the processes of innovation and the diffusion of new financial instruments.