Fintech firms mobilize information technology to provide intermediation services using a broker methodology, whereas dealer banks intermediate using leveraged balance sheets. The integration of Fintech into banking may reduce the unit cost of intermediation by shifting the production function from dealer to broker. A “Fintech score” is derived using nonlinear and machine learning algorithms that show on-balance sheet lending for low Fintech score dealer banks versus securitization, brokered deposits, and non-interest income for high score, broker banks. Using Data Envelopment and Stochastic Cost Frontier Analyses, we find that banks with higher Fintech scores are more operationally efficient and resilient in crises.

We propose a model of trading in the over-the-counter corporate bond market where investors can buy and sell bonds through a dealer and can short bonds by borrowing them in the securities lending market. The model predicts that higher dealer inventory costs are associated with lower short interest for bonds, particularly for high-credit-quality bonds. We construct bond-level proxies for inventory costs and provide empirical evidence in support of the model's prediction. We find that much of the dramatic decline in short interest observed since the Great Financial Crisis (GFC) can be explained by an increase in proxies for inventory costs. We document that the short-sale constraints imposed by higher dealer inventory costs have had a negative impact on price efficiency. Our findings suggest that tighter post-GFC regulation may have had unintended consequences for bond market quality.

Using Granger Causal and Cointegration Tests, we analyzed the relationship between the open interests of commercial investors, non-commercial invest institution, retail dealers and the oil price in WTI futures market from 2003 to 2012. Found the short positions of commercial investors are determined by the basis and futures price. And the long positions held by commercial investors are for the need of commercial investors and invest institution to hold short positions. The investment institutions adjust the long positions by the change of the futures price. There are the co-integration relationship between the positions hold by Commercial customers and investment institutions. And the changes of short positions hold by investment institutions are affected by the long positions hold by investment institutions. The long positions holds by the commercial investors are affected by the short positions hold by the commercial investors and investment institutions. These founding can help us to understand the relationship and internal influences mechanism between the open interests and price in futures markets.

As the launch of the China’s first stock index futures (SIF) approaches with no exact date for its eventual introduction. The Chinese stock market has increased dramatically due to this expectation recently, especially the futures contracts related stocks have raised significantly which are good examples of this influence. As the stock index futures is a new financial product, Chinese investors cannot help wondering whether the launch of the stock index future will have a positive or negative impact upon the underlying stock market. On the other hand, the new instruments which, will be followed by the introduction of other derivatives, will require broker-dealers to upgrade their systems and invest in new technology. Therefore, it has become pertinent to investigate the opportunities and challenges this eagerly awaited derivative instrument has to offer to fund managers in the booming Chinese economy.