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Purpose – The purpose of this paper is to study the pricing efficiency of convertible bonds and arbitrage opportunities between the convertible bonds and the underlying stocks thus improve market efficiency. Design/methodology/approach – Using nonparametric fixed effect panel data model, the authors build pricing model of convertible bonds and obtain fitted value for them. Then the authors constructs simultaneous confidence band for the smooth function to identify mispricing and study the pricing efficiency and arbitrage opportunities of convertible bonds. Findings – Result shows, convertible bonds’ prices largely depend on stock prices. Pricing efficiency does not improve during the past few years as there are quite a few trading opportunities. Arbitrage opportunities increase as the stock prices approach it maxima, and selling opportunities for convertible bonds surpass buying opportunities which indicates that investors use market neutral strategies to arbitrage. Pricing efficiencies varies a lot and it is affected by the features of the stocks and convertible bonds. Index stocks eligible for margin trading with high liquidity enjoy higher pricing efficiency. Research limitations/implications – The study does not take into account trading cost and risk management measures. Practical/implications – Arbitrage between the underlying and the convertible bonds is profitable and contributes to pricing efficiency therefore should be encouraged. The regulator should pay attention to the extreme mispricing of the underlying and convertible bonds which cannot be corrected by the market as there might be manipulation. Originality/value – Since traditional pricing methods are based on the framework of non-arbitrage equilibrium with the assumption of balanced and perfect market, there are many restrictions in the pricing process and the practical utility is somewhat limited, and the impractical assumptions lead to model risk. This study uses nonparametric regression to study the pricing of convertible bonds thus circumvents the problem of model risk. Simultaneous confidence band for smooth function identifies mispricing and explicitly reflects the variation of pricing efficiency as well as signalizes trading opportunities. Application of nonparametric regression and simultaneous confidence band in derivative pricing is advantageous in accuracy and simplicity.

Purpose The prospect theory is potentially an essential ingredient in modeling the disposition effect. However, many scholars have tried to explain the disposition effect with the help of prospect theory and they came to opposite conclusions. The purpose of this paper is to examine the impact of value function of the prospect theory on predicting the disposition effect. Design/methodology/approach Lagrange multiplier optimization and dynamic programming method are used to solve the representative investor’s optimal portfolio choice problem. Furthermore, numerical simulation is used to compare the prediction ability of different types of value function. Findings The authors support that the value function has a crucial role in predicting the disposition effect with prospect theory, i.e. the curvature and boundedness of the value function may influence the performance of applying the prospect theory in the disposition effect. They conclude that a piecewise negative exponential value function can predict the disposition effect, while others like the piecewise power value function may not. Originality/value Extant literature about modeling the disposition effect with the prospect theory mostly focus on the time when gain-loss utility occurs or the selection of reference point. This paper based on the value function properties provides a new perspective in analyzing the crucial role that value function has in predicting financial market anomalies.

Three major types of Ordovician intrusive‐related gold‐copper deposits are recognized in central‐west New South Wales, Australia: porphyry, skarn and high sulphidation epithermal deposits. These deposits are mainly distributed within two Ordovician volcano‐intrusive belts of the Lachlan Fold Belt: the Orange‐Wellington Belt and the Parkes‐Narromine Belt. Available isotopic age data suggest that mineralization of the three types of deposits is essentially coeval with the Ordovician intrusive rocks (480–430 Ma).Porphyry gold‐copper deposits can be further divided into two groups. The first group is associated with monzonite showing shoshonitic features, represented by Cadia and Goonumbla. The second group is associated with diorite and dacite, including the Copper Hill and Cargo gold‐copper deposits. Gold skarn is associated with Late Ordovician (430–439 Ma) monzonitic intrusive complexes in the Junction Reefs area (Sheahan‐Grants, Frenchmans, and Cornishmens), Endeavour 6, 7 and 44, Big and Little Cadia. The epithermal gold deposits with high sulphidation including Gidginbung (Temora) and Peak Hill mainly occur within Ordovician andesite and volcaniclastic rocks, and are associated with advanced argillic alteration. Available isotopic age data indicate that both alteration and mineralization of the porphyry, skarn and epithermal gold‐copper deposits are broadly coeval with the Late Ordovician shoshonitic magmatism, which is thought to result from the melting of sub‐continental lithosphere caused by Palaeozoic subduction events.The Ordovician intrusive‐related gold‐copper deposits are restricted to two longitudinal parallel volcano‐intrusive belts, rarely extending outside them. Diagonal intra‐belt trends of mineralization are common, particularly at the intersections of longitudinal and transverse (oblique) fault/fracture zones based on the authors' review of available geological data. The locations of these gold‐copper deposits are obviously influenced by transverse (oblique) fault/fracture zones that are oriented northwest, eastwest and northeast. The conjunctions of these fault/fractures zones are thought to be zones of structural weakness, and appear to be the favourable locus for the Ordovician intrusive‐related gold‐copper deposits. Differences in structural patterns, intrusive, wall rock types, and depths of ore formation may contribute to the differences among the deposits.