Learning from market prices by decision-makers in the real side of the economy affects informed investor trading behavior and financial market efficiency, which feedback to managers' decisions and real efficiency. Such a feedback loop provides some interesting results on what could be equilibrium outcomes in the financial markets. In this doctoral dissertation, I study the role managerial learning from financial market prices plays in various financial and economic settings.

Chapter 1 studies the dark pool effects on price discovery and real efficiency when firm managers who need to make production investment decisions learn information from financial market prices. A strategic informed investor trades on private information and chooses a trading venue between an exchange market and a dark pool. An uninformed noise investor randomly selects a trading venue and randomly trade. Managerial learning leads to wiser real decisions and results in higher firm value, which aggravates the buying profits and alleviates the short-selling profits of the informed investor. The magnitude of managerial learning effects and the execution risk of dark pool trading vary in the noise trading in the exchange market. As a result, the dark pool effects on informed investor trading venue choice, exchange market efficiency, and real efficiency all depend on the noise trading in the exchange market.

Chapter 2 investigates the dark pool effects on investor trading venue choice in a model featuring managerial learning from exchange market prices. The model is essentially the one studied in Chapter 1 with transaction cost in the exchange market and delay cost of the uninformed liquidity investor that make the uninformed liquidity investor trading venue choice endogenous. While the transaction cost and the delay cost affect the informed trading and liquidity trading in the exchange market, the dark pool does not divert investors away from the exchange market and thus does not affect the exchange market efficiency. However, the dark pool may initiate investors' coordination motives to trade in the dark pool whenever trading in the exchange market can not bring higher profits than choosing not to trade.

Chapter 3 analyzes the interaction between secondary financial market efficiency and product market competition in an entry game. A potential entrant learns from an insider's trading in the stock market of a monopoly incumbent, such that the insider and the entrant have conflicting interests. Once the entrant enters, it competes with the incumbent in a Cournot duopoly setting and reduces the incumbent firm value. As a result, entrant learning causes ``buy-side'' limits to arbitrage. Depending on different entry barriers, transaction costs in the financial market may increase or decrease entry probability. The impact of transaction costs on the entry probability is also affected by economic and informational conditions that the insider faces. A policy of reducing entry barriers has non-monotonic effects on entry probability.

Affordable consumer drones have made capturing aerial footage more convenient and accessible. However, shooting cinematic motion videos using a drone is challenging because it requires users to analyze dynamic scenarios while operating the controller.

In this thesis, our task is to develop an autonomous drone cinematography system to capture cinematic videos of human motion. We understand the system's filming performance to be influenced by three key components: 1) video quality metric, which measures the aesthetic quality -- the angle, the distance, the image composition -- of the captured video, 2) visual feature, which encapsulates the visual elements that influence the filming style, and 3) camera planning, which is a decision-making model that predicts the next best movement. By analyzing these three components, we designed two autonomous drone cinematography systems using both heuristic-based methods and learning-based methods.

For the first system, we designed an Autonomous CinemaTography system -- "ACT" by proposing a viewpoint quality metric focusing on the visibility of the 3D human skeleton of the subject. We expanded the application of human motion analysis and simplified manual control by assisting viewpoint selection using a through-the-lens method. For the second system, we designed an imitation-based system that learns the artistic intention of the cameramen through watching professional aerial videos. We designed a camera planner that analyzes the video contents and previous camera motion to predict future camera motion. Furthermore, we propose a planning framework, which can imitate a filming style by ``seeing" only one single demonstration video of such style. We named it ``one-shot imitation filming." To the best of our knowledge, this is the first work that extends imitation learning to autonomous filming. Experimental results in both simulation and field test exhibit significant improvements over existing techniques and our approach managed to help inexperienced pilots capture cinematic videos.